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Eija Suorsa

OULU 2011

D 1118

Eija Suorsa

ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY

UNIVERSITY OF OULU,FACULTY OF MEDICINE,INSTITUTE OF CLINICAL MEDICINE,DEPARTMENT OF NEUROLOGY

A C T A U N I V E R S I T A T I S O U L U E N S I SD M e d i c a 1 1 1 8

EIJA SUORSA

ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY

Academic dissertation to be presented with the assent ofthe Faculty of Medicine of the University of Oulu forpublic defence in Auditorium 8 of Oulu UniversityHospital, on 11 November 2011, at 12 noon

UNIVERSITY OF OULU, OULU 2011

Copyright © 2011Acta Univ. Oul. D 1118, 2011

Supervised byDocent Jouko IsojärviDocent Juha Korpelainen

Reviewed byProfessor Torbjörn TomsonDocent Aarne Ylinen

ISBN 978-951-42-9554-6 (Paperback)ISBN 978-951-42-9555-3 (PDF)

ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2011

Suorsa, Eija, Assessment of heart rate variability as an indicator of cardiovascularautonomic dysregulation in subjects with chronic epilepsy. University of Oulu, Faculty of Medicine, Institute of Clinical Medicine, Department ofNeurology, P.O. Box 5000, FI-90014 University of Oulu, FinlandActa Univ. Oul. D 1118, 2011Oulu, Finland

AbstractAutonomic dysfunction in epilepsy is widely recognized. Both partial and generalized epilepsiesaffect autonomic functions during interictal, ictal and postictal states. Interestingly, there isincreasing evidence of interictal autonomic nervous system dysfunction as evidenced by reducedheart rate (HR) variability in patients with epilepsy. Reduced HR variation has also been detectedin many other chronic diseases and it has been shown to be associated with unfavourable prognosiswith an increased risk of mortality in various heart diseases. Recently, more attention has also beenpaid to possible association of decreased HR variability with sudden unexpected death in epilepsy(SUDEP). However, the clinical significance of the observed changes in cardiovascular regulationin patients with epilepsy is still poorly outlined and there are no long-term studies about changesin HR variation in relation to epilepsy.

This study was designed to evaluate long-term changes in autonomic cardiovascular regulationin patients with temporal lobe epilepsy (TLE) and also to evaluate HR variation during vagusnerve stimulation (VNS) treatment in patients with refractory epilepsy, using 24-hour ambulatoryECG recordings. Special attention was paid to changes in HR variation and to circadian HRfluctuation over time.

The results of this study show that autonomic cardiovascular regulation is affected both inpatients with well-controlled TLE and in patients with refractory TLE, and that the cardiovasculardysregulation also presents itself with changes in circadian HR variability, with more pronouncedalterations observed during the night time. HR variability was also found to decrease progressivelywith time in patients with chronic refractory TLE with uncontrolled seizures. VNS treatment wasnot observed to alter HR variation.

Keywords: autonomic nervous system, heart rate variation, sudden unexpected death inepilepsy, temporal lobe epilepsy, vagus nerve stimulation

Suorsa, Eija, Sydämen sykevaihtelu kroonisessa epilepsiassa. Oulun yliopisto, Lääketieteellinen tiedekunta, Kliinisen lääketieteen laitos, Neurologia, PL5000, 90014 Oulun yliopistoActa Univ. Oul. D 1118, 2011Oulu

TiivistelmäEpilepsiapotilailla esiintyy autonomisen hermoston toiminnan häiriöitä. Näitä häiriöitä voidaantodeta epilepsiakohtausten aikana, heti kohtausten jälkeen ja kohtausten välillä sekä paikallisal-kuisissa että yleistyneissä epilepsioissa. Viimeaikaisissa tutkimuksissa on osoitettu kardiovasku-laarisen säätelyjärjestelmän häiriöiden voivan ilmentyä alentuneena sydämen sykevaihtelunaepilepsiakohtausten väliaikoina. Sydänsairauksien yhteydessä sykevaihtelun vähenemisen onosoitettu liittyvän kohonneeseen kuolemanriskiin. Epilepsiapotilailla alentuneen sydämen syke-vaihtelun on epäilty liittyvän epilepsiapotilailla ilmenevien odottamattomien ja ilman selkeääsyytä tapahtuvien äkkikuolemien (SUDEP) lisääntyneeseen riskiin. Kertyneestä tiedosta huoli-matta alentuneen sykevaihtelun kliininen merkitys epilepsiapotilailla on edelleen epäselvä. Pit-käaikaisseurantatutkimuksia sydämen sykevaihtelun muutoksista epilepsiapotilailla ei ole jul-kaistu.

Tämän tutkimuksen tarkoituksena oli selvittää ohimolohkoepilepsiaan liittyviä pitkäaikaisiainteriktaalisia (kohtausten välillä esiintyviä) kardiovaskulaarisia ilmentymiä. Lisäksi haluttiintutkia vaikeahoitoisessa epilepsiassa käytetyn hoitomuodon, vagushermostimulaation, mahdolli-sia vaikutuksia sydämen toimintaan. Erityisesti haluttiin analysoida sykevaihtelun vuorokausi-rytmiä.

Tulokset osoittavat autonomisen hermoston kardiovaskulaarisen säätelyjärjestelmän toimin-nan olevan häiriintyneen sekä vaikeahoitoisilla että hyvähoitoisilla ohimolohkoalkuista epilepsi-aa sairastavilla potilailla. Sydämen sykevariaatio on alentunut erityisesti yöaikaan. Lisäksi sydä-men sykevaihtelu alenee pitkäaikaisseurannassa vaikeahoitoista epilepsiaa sairastavilla potilail-la, joilla ilmenee toistuvia epileptisiä kohtauksia. Vagusstimulaatio ei aiheuttanut muutoksiasyketaajuuden vaihteluun.

Asiasanat: autonominen hermosto, ohimolohko epilepsia, sydämen sykevaihtelu,vagushermo stimulaattori, äkkikuolema

To my family

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Acknowledgements

The research for this thesis was carried out at the Department of Neurology, at the

University of Oulu, during the years 1999–2011.

I wish to express my warmest thanks to my supervisor, Docent Jouko

Isojärvi, for introducing me to the world of epilepsy science with such expertise.

Your positive and enthusiastic attitude has given me inspiration and guided me

over the difficult times as well. I also want to thank my supervisor Docent Juha

Korpelainen, who provided his practical advice and shared his scientific

knowledge. You always had time for discussions and your tireless encouragement

made it possible to finish this work. Thanks to my supervisor’s encouragement,

logical thinking and supportive responsibility distribution, I have found the world

of science fascinating and challenging. It has been a privilege to work with you.

I wish to express my gratitude to Professor Vilho V. Myllylä, for his guidance

and sympathetic support for my study. His broad experience of science has

created an inspiring atmosphere to work in. I am also very grateful to Professor

Matti Hillbom and Professor Kari Majamaa for providing research facilities for

scientific work.

I wish to thank Professor Heikki Huikuri for his excellence guidance in the

field of heart rate variability analysis, and Pirkko Huikuri for her valuable

technical assistance in heart rate variability data processing. I also wish to thank

Professor Esa Heikkinen for his expertise in the field of neurosurgery with vagus

nerve stimulation. I also thank Docent Kyösti Sotaniemi for his kind

encouragement and interest in my research work.

I wish to acknowledge most sincerely Professor Torbjörn Tomson and Docent

Aarne Ylinen for their expertise and valuable comments during the preparation of

the final manuscript for this thesis. I also feel honoured that Professor Tapani

Keränen kindly agreed to serve as my opponent. I express my appreciation to

Anna Vuolteenaho for the careful revision of the English language of the

manuscript.

I owe my thanks to the entire staff of the Departments of Neurology and

Division of Cardiology for their excellent co-operation through the years of this

study. Special thanks are due to Ilona Huovinen for her friendly secretarial

assistance throughout the study. I would also like to thank Risto Bloigu for his

expertise with statistical analyses. I wish to express my sincere gratitude to the

patients and their families who made this work possible.

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I warmly thank my colleagues in epilepsy research, Johanna Rättyä, Hanna

Ansakorpi, Virpi Pylvänen, Eeva Löfgren, Katja Luoma, Kirsi Mikkonen and

Usko Huuskonen, for their interest in my project as well as for your friendship

and inspiring conversation. I warmly thank Jaana Huttula and Paula Kelhä for

their friendship and assistance.

It is my pleasure to thank Peter Baumann for introducing me to the world of

clinical neurology. It was such a pleasure to work with you.

I thank all my dear and cheerful friends, who have made life more interesting

and enjoyable for me. Henna Mustaniemi, Milla Riski and Anne Mäntyniemi,

deserves my loving thanks for friendship and laughs. You have a special place in

my heart! My parents-in-law, Sinikka and Esko Suorsa are warmly thanked for

their endless belief that I would finish this work, and also for practical help with

the care of our children.

I thank my sister Piia Pitkänen for your love, support and laughter that have

kept me going. Your own projects, Ida and Oskari, are my sunshine. I owe my

greatest gratitude to my wonderful parents Salme and Veikko Ronkainen for

providing me with a supportive family and for advising me when needed. Your

enormous encouragement and belief in me have made lots of things happen.

Finally, my dearest thanks are expressed to my life companion Ville Suorsa.

Not only for your work for this thesis but also your patience and love. We have

been blessed with two special girls, Ella and Anni. Love you.

This research project was supported by grants from the Epilepsy Research

Foundation, the Research Foundation of Orion Corporation, the Centre for Arctic

Medicine and the Oulu Medical Research Foundation. All these are warmly

acknowledged.

Oulu, September 2011 Eija Suorsa

11

Abbreviations

α short-term scaling exponent

β slope of the power-law relationship

AED antiepileptic drug

ANS autonomic nervous system

ApEn approximate entropy

CBZ carbamazepine

CNS central nervous system

CT computerized tomography

ECG electrocardiography

EEG electroencephalography

GABA gamma-amino butyric acid

GBP gabapentin

HF high frequency

HR heart rate

LF low frequency

LEV levetiracetam

LTG lamotrigine

MRI magnetic resonance imaging

NTS nucleus tractus solitarius

OXC oxcarbazepine

PHT phenytoin

RR interval R-peak-to-R-peak interval

SD1 instantaneous beat-to-beat RR interval variability

SD2 long-term continuous RR interval variability

SDNN standard deviation of all RR intervals

SUDEP sudden unexpected death in epilepsy

TGB tiagabine

TLE temporal lobe epilepsy

TPM topiramate

VGB vigabatrin

VLF very low frequency

VNS vagus nerve stimulation/stimulator

VPA valproate

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13

List of original articles

This thesis is based on the following publications, which are cited in the text by

their Roman numerals:

I Ronkainen E, Ansakorpi H, Huikuri HV, Myllylä VV, Isojärvi JIT & Korpelainen JT (2005) Suppressed circadian heart rate dynamics in temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 76(10): 1382–1386.

II Suorsa E, Korpelainen JT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Isojärvi JIT (2011) Heart rate dynamics in temporal lobe epilepsy – a long term follow-up study. Epilepsy Research 93(1): 80–83.

III Suorsa E, Isojärvi JIT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Korpelainen JT (2011) Long-term changes in circadian heart rate variability in patients with temporal lobe epilepsy. Manuscript

IV Ronkainen E, Korpelainen JT, Heikkinen E, Myllylä VV, Huikuri HV & Isojärvi JIT (2006) Cardiac autonomic control in patients with refractory epilepsy before and during vagus nerve stimulation treatment – a one year follow-up study. Epilepsia 47(3): 556–562.

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15

Contents

Abstract

Tiivistelmä

Acknowledgements 7 Abbreviations 11 List of original articles 13 Contents 15 1 Introduction 17 2 Review of the literature 19

2.1 General aspects of epilepsy ..................................................................... 19 2.1.1 Definition...................................................................................... 19 2.1.2 Epidemiology ............................................................................... 19 2.1.3 Aetiology ...................................................................................... 20 2.1.4 Classification ................................................................................ 20 2.1.5 Diagnosis ...................................................................................... 24 2.1.6 Prognosis ...................................................................................... 25

2.2 Temporal lobe epilepsy ........................................................................... 25 2.3 Treatment of epilepsy .............................................................................. 26

2.3.1 Antiepileptic drugs ....................................................................... 26 2.3.2 Surgery ......................................................................................... 31 2.3.3 Vagus nerve stimulation ............................................................... 32

2.4 Autonomic nervous system ..................................................................... 34 2.4.1 Anatomy of the autonomic nervous system ................................. 34 2.4.2 Cardiovascular regulation ............................................................. 35

2.5 Heart rate variability and its clinical implications .................................. 39 2.5.1 Physiological background of heart rate variability and

heart rate dynamics ....................................................................... 39 2.5.2 Factors affecting heart rate variability .......................................... 41 2.5.3 Heart rate variability in pathological conditions .......................... 42

2.6 Epilepsy and autonomic cardiovascular dysregulation ........................... 43 2.6.1 Ictal autonomic dysfunction ......................................................... 43 2.6.2 Interictal heart rate variation ........................................................ 44 2.6.3 Circadian heart rate variation ....................................................... 45 2.6.4 Effect of vagus nerve stimulation on cardiovascular

autonomic function ....................................................................... 46 2.7 Sudden unexpected death in epilepsy ..................................................... 47

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2.7.1 Definition ...................................................................................... 47 2.7.2 Epidemiology ............................................................................... 47 2.7.3 Aetiology ...................................................................................... 48

3 Aims of the study 51 4 Subjects and methods 53

4.1 Subjects ................................................................................................... 53 4.2 Methods ................................................................................................... 56

4.2.1 Clinical examination (Studies I-IV) .............................................. 56 4.2.2 Adjustment and use of vagus nerve stimulator (Study IV) ........... 56 4.2.3 Analysis of heart rate behaviour (Studies I-IV) ............................ 57 4.2.4 Statistical analysis ......................................................................... 59

5 Results 61 5.1 Clinical evaluation of autonomic nervous system function .................... 61 5.2 Cardiac regulation in temporal lobe epilepsy .......................................... 61

5.2.1 Long-term heart rate dynamics (Study II) .................................... 61 5.2.2 Circadian heart rate variation (Study I) ........................................ 62 5.2.3 Long-term changes in circadian heart rate variation (Study

III) ................................................................................................. 65 5.3 Effect of vagus nerve stimulation on heart rate dynamics (Study

IV) ........................................................................................................... 68 6 Discussion 71

6.1 General aspects........................................................................................ 71 6.2 Clinical findings of autonomic nervous system function in

patients with epilepsy .............................................................................. 72 6.3 Cardiac regulation in temporal lobe epilepsy .......................................... 72

6.3.1 Long-term heart rate dynamics ..................................................... 72 6.3.2 Circadian heart rate variation ....................................................... 73 6.3.3 Long-term changes in circadian heart rate variation .................... 75 6.3.4 Effect of antiepileptic medication on heart rate variation ............. 76

6.4 Effect of vagus nerve stimulation on heart rate dynamics....................... 76 6.5 Methodological considerations ............................................................... 77

7 Conclusions 79 References 81 Original publications 101

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1 Introduction

Epilepsy is a descriptive term for a large group of anatomical and functional

disorders of the brain that are characterized by repeated seizures. Epilepsy is one

of the most common serious brain disorders. Throughout history, epilepsy has

been associated with superstition and stigma, but over the past decades scientists

have made significant advances in the understanding and treatment of this

disorder. Simultaneously, public knowledge of this disorder has improved, and

attitudes have slowly changed towards its acceptance.

The prevalence of active epilepsy is estimated at 3–8 per 1,000 of the

population according to European studies (Keränen et al. 1989, Olafsson &

Hauser 1999, Rocca et al. 2001). The treatment of epilepsy is mainly based on

drug treatment. However, for those who do not achieve seizure freedom despite

adequate antiepileptic drug (AED) treatment epilepsy surgery may be a

therapeutic alternative. With appropriate treatment, 70–75% of patients with

epilepsy become seizure-free, but the remainder will be resistant to treatment and

classified as patients with refractory epilepsy (Sander & Shorvon 1996, Cockerell

et al. 1997, Kwan & Brodie 2000, Kwan & Sander 2004). Electrical stimulation

of the vagus nerve is a treatment for patients with refractory epilepsy who are

unsuitable candidates for resective surgery or who have experienced insufficient

benefit from such treatment (Uthman et al. 1993, Ben-Menachem 2002). Despite

appropriate treatment, patients with epilepsy have mortality rates that are 2–3

times higher compared to general population (Olafsson et al. 1998, Tomson

2000).

Epilepsy may affect autonomic nervous system (ANS) function during

interictal (between seizures), ictal (during seizures) and postictal (after seizures)

states. Cardiac function may be altered in patients with epilepsy as a result of

autonomic dysfunction at several different levels, including central and peripheral

ANS pathways, and also at the level of the heart. It is not clear whether the

observed imbalance of the sympathetic and parasympathetic input to the heart in

patients with epilepsy is due to epilepsy per se or whether other factors, such as

medication or its withdrawal, may play a role as well (Isojärvi et al. 1998,

Tomson et al. 1998, Hennessy et al. 2001, Ansakorpi et al. 2002).

Traditional time and frequency domain measures of heart rate (HR)

variability along with the newer methods based on fractal and complexity scaling

of RR interval variability have both been used as non-invasive tools for assessing

autonomic cardiovascular regulation (Task Force 1996). Using conventional

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short- and long-term ECG recordings, previous studies have reported diminished

interictal HR variability in patients with epilepsy, mainly temporal lobe epilepsy

(TLE), but it has remained unclear whether the observed reduction in

cardiovascular responses is due to the epileptic process itself or to the AEDs

(Frysinger et al. 1993, Isojärvi et al. 1998, Tomson et al. 1998, Ansakorpi et al.

2000, Ansakorpi et al. 2002). On the other hand, low HR variability has also been

reported in a number of other pathophysiologic conditions (Bernardi et al. 1992,

Huikuri et al. 1994, Korpelainen et al. 1997), and has been shown to be a marker

of an increased risk of mortality in patients with these conditions (Kleiger et al.

1987, Huikuri et al. 1994, Malliani et al. 1994, Barron & Viskin 1998).

Patients with epilepsy are at increased risk for SUDEP, and a large body of

data has defined different risk factors for SUDEP, e.g. youth, polytherapy with

AEDs, lack of compliance with treatment and poor seizure control (Devinsky et

al. 1994, Nilsson et al. 1999, Opeskin et al. 2000, Tomson et al. 2008). However,

otherwise healthy, compliant patients may also die unexpectedly (Earnest et al.

1992, Nashef et al. 1998). Recently, more attention has been paid to possible

association between altered cardiovascular function and SUDEP. It has been

suggested that reduced HR variability may play a role in the pathophysiology of

sudden unexpected death in epilepsy (SUDEP) in patients with chronic epilepsy

(Tomson et al. 1998). However, according to one recent study no HR variability

parameter was associated with SUDEP, suggesting that HR variability parameters

are not clear-cut predictors for SUDEP (Surges et al. 2009a).

There are no previous longitudinal studies on changes in autonomic

cardiovascular regulation in patients with TLE over time. There is also limited

information on the circadian HR variation in this patient group. Furthermore,

there are only few published short-term studies on the effects of VNS treatment

on cardiovascular regulation despite the close interaction between VNS and the

centres controlling cardiovascular regulation.

The present study was designed to evaluate long-term cardiovascular

autonomic regulation in patients with TLE and in patients with VNS treatment by

using 24-hour ECG recordings.

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2 Review of the literature

2.1 General aspects of epilepsy

2.1.1 Definition

Epilepsy is the name of a brain disorder characterized predominantly by recurrent

and unpredictable interruptions of normal brain function, called epileptic seizures.

Epileptic seizures can affect sensory, motor, and autonomic function, which are

characterized by various clinical manifestations such as decrease of

consciousness, abnormal sensory phenomena, increased autonomic activity and

involuntary movements. Epilepsy is not a singular disease entity but a dynamic

process, which reflects complex functional changes occurring in the anatomy and

physiology of the brain in the presence of environmental and genetic factors.

(Waltimo 1983, Fisher et al. 2005)

2.1.2 Epidemiology

Epilepsy is the most common chronic disorder of the central nervous system

(CNS). Numerous studies have been published on the epidemiology of epilepsy.

However, the study designs and the definition of epilepsy and seizure types differ

from one study to another, which makes comparison between different studies

difficult.

Prospective, population-based studies indicate that in general population

there is an 8–10% lifetime risk of one seizure (Hauser et al. 1990) and a 3%

chance of epilepsy (Hauser et al. 1993). The incidence of epilepsy is 24–53

/100,000 person years in developed countries (Keränen et al. 1989, Olafsson et al.

1996, Zarrelli et al. 1999, MacDonald et al. 2000), whereas in developing

countries it is considered to be higher. The incidence is high in childhood and

increases again in elderly people (Sillanpää 1973, Keränen et al. 1989, Hauser et

al. 1993, Forsgren et al. 1996, Olafsson et al. 1996, Beilmann et al. 1999).

Interestingly, a recent population-based study found that the incidence of epilepsy

in Finland has declined significantly in both children and adults with a concurrent

increase in incidence among the elderly (Sillanpää et al. 2006), but the reasons for

the changes in incidence remained unclear.

The prevalence of active epilepsy, often defined as patients with epilepsy who

have had at least one seizure during the last 5 years, is 3.3–7.8 per 1000

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inhabitants in studies performed in European countries (Granieri et al. 1983,

Keränen et al. 1989, Olafsson & Hauser 1999, Rocca et al. 2001). In most

previous studies the prevalence of epilepsy has been higher in males than females

(Granieri et al. 1983, Hauser 1997, Olafsson & Hauser 1999, Rocca et al. 2001).

However, the prevalence difference between genders has rarely been shown to be

statistically significant (Granieri et al. 1983, Keränen et al. 1989). According to

the Finnish Social Insurance Institution, 58,594 patients out of a total population

of approximately 5 million received reimbursement for antiepileptic medication

in Finland in 2009. Approximately 9,000 of these patients suffer from medically

intractable epilepsy. (Social Insurance Institution 2006)

2.1.3 Aetiology

Almost any cerebral pathology may be associated with epilepsy. In the adult

population the cause of epilepsy is unknown in the majority of patients (Beghi

2004). The most common causes of epilepsy are cerebrovascular diseases, head

trauma, intracranial haemorrhages, cerebral tumours and neurodegenerative

diseases. (Sander et al. 1990, Forsgren et al. 1996, Olafsson et al. 1996, Oun et

al. 2003). In children metabolic defects, congenital malformations, infections and

genetic diseases are among common aetiologies (Beghi 2004). It seems that

almost everyone may experience a seizure in a particular set of circumstances, but

some people seem to have a lower seizure threshold than others.

2.1.4 Classification

Classification of epileptic seizures

Epileptic seizures can be classified in several different ways. Their electroclinical

features identify them as either partial or generalized. Partial seizures start in a

circumscribed set of nerve cells (the “epileptic focus”) in one hemisphere of the

brain and spread from there. Generalized seizures involve both sides of the brain

from the onset, although they may sometimes involve only a small part of the two

hemispheres in a symmetrical manner. (ILAE 1981) Table 1 presents 1981 ILAE

classification of seizures, which was used in the present thesis. However, new

terminology and classification has been established since the start of this research

(ILAE 2005–2009) (Berg et al. 2010).

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According to new classification of epilepsies, the diagnostic scheme provides

the basis for a standardized description of individual patients, and consists of five

levels. The levels are organized to facilitate a logical clinical approach to the

development of hypotheses necessary to determine the diagnostic studies that

should be performed in individual patients. The levels are 1) ictal phenomenology

2) epileptic seizure type/types 3) epileptic syndrome 4) aetiology and 5) the

degree of impairment caused by the epileptic condition.

Table 1. International classification of seizures (Commission on Classification and

Terminology, ILAE, 1981).

Class Classification

I Partial (focal) seizures

A Simple partial seizures (consciousness not impaired)

1. With motor signs

Focal motor without march

Focal motor with march (Jacksonian)

Versive

Postural

Phonatory (vocalization or arrest of speech)

2. With somatory or special-sensory symptoms (simple hallucinations, e.g. tingling, light

flashes, buzzing)

Somatosensory

Visual

Auditory

Olfactory

Gustatory

Vertiginous

3. With autonomic symptoms or signs (including epigastric sensation, pallor, sweating,

flushing, piloerection, and pupillary dilatation)

4. With psychic symptoms (disturbances of higher cerebral functions); these symptoms rarely

occur without impairment of consciousness and are much more commonly experienced as

complex partial seizures

Dysphasic

Dynamic (e.g. déjà vu)

Cognitive (e.g. dreamy states, distortions of time sense)

Affective (fear, anger, etc.)

Illusions (e.g. macropsia)

Structured hallucinations (e.g. music, scenes)

B. Complex partial seizures (with impairment of consciousness; may sometimes begin with

simple symptomatology)

1. Simple partial onset followed by impairment of consciousness

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Class Classification

With simple partial features (A.1. - A.4.) followed by impaired consciousness

With automatisms

2. With impairment of consciousness at onset

With impairment of consciousness only

With automatisms

C. Partial seizures evolving to secondary generalized seizures (may be generalized tonic-

clonic, tonic, or clonic)

1. Simple partial seizures (A) evolving to generalized seizures

2. Complex partial seizures (B) evolving to generalized seizures

3. Simple partial seizures evolving to complex partial seizures evolving to generalized seizures

II Generalized seizures (convulsive or non-convulsive)

A.

1. Absence seizures

Impairment of consciousness only

With mild clonic components

With atonic components

With tonic components

With automatisms

With autonomic components

2. Atypical absence

May have:

Changes in tone that are more pronounced than in A.1.

Onset and/or cessation that is not abrupt

B. Myoclonic seizures

Myoclonic jerks (single or multiple)

C. Clonic seizures

D. Tonic seizures

E. Tonic-clonic seizures

F. Atonic seizures

III Unclassified seizures

Includes all seizures that cannot be classified because of inadequate or incomplete data.

Classification of epilepsies and epileptic syndromes

Epilepsies and epileptic syndromes have been classified according to aetiology

and anatomic origin of seizures. Symptomatic epilepsies are due to a recognizable

insult to the brain (e.g. resulting from a malformation, trauma, or tumour). In

idiopathic epilepsies, a known cause cannot be identified, but they are commonly

caused by a genetic defect. If a symptomatic aetiology is suspected but cannot be

demonstrated, the condition is called cryptogenic epilepsy. (Shorvon et al. 2004)

23

Table 2 presents the 1989 ILAE classification of epilepsies and epileptic

syndromes, which is used in this thesis. According to new proposed terminology

and classification (ILAE 2005–2009) genetic, structural-metabolic and unknown

represent modified concepts to replace idiopathic, symptomatic and cryptogenic.

Effective classification of seizures and syndromes is indispensable for adequate

therapy and prognosis.

Table 2. International classification of epilepsies and epileptic syndromes

(Commission on Classification and Terminology, ILAE, 1989).

Class Classification

1. Localization-related (focal, local, partial)

1.1. Idiopathic (with age-related onset)

Benign childhood epilepsy with centrotemporal spikes

Childhood epilepsy with occipital paroxysm

Primary reading epilepsy

1.2. Symptomatic

Chronic progressive epilepsia partialis continua of childhood

1.3. Cryptogenic

The symptomatic and cryptogenic categories comprise syndromes of great individual

variability that are based on:

Seizure types (according to the International classification of Epileptic Seizures)

Anatomic localization: Temporal, frontal, parietal, and occipital lobe epilepsies

Bi- and multilobar epilepsies

Aetiology (in symptomatic epilepsies)

Specific modes of precipitation

2. Generalized

2.1. Idiopathic (with age-related onset, in order of age)

Benign neonatal familial convulsions

Benign neonatal convulsions

Benign myoclonic epilepsy of infancy

Childhood absence epilepsy (pyknolepsy)

Juvenile absence epilepsy

Juvenile myoclonus epilepsy (impulsive petit mal)

Epilepsy with grand mal (GTC) seizures on awaking

Other idiopathic generalized epilepsies not defined above

Epilepsies with seizure precipitated by specific modes of activation

2.2. Cryptogenic or symptomatic (in order of age)

West syndrome (infantile spasms, Blitz-Nick-Salaam-Krämpfe)

Lennox-Gastaut syndrome

Epilepsy with myoclonic-astatic seizures

Epilepsy with myoclonic absences

24

Class Classification

2.3. Symptomatic

2.3.1. Non-specific aetiology

Early myoclonic encephalopathy

Early infantile epileptic encephalopathy with suppression-burst

Other symptomatic generalized epilepsies not defined above

2.3.2 Specific syndromes (see the original reference)

3. Epilepsies and syndromes undetermined whether focal or generalized

3.1. With both generalized and focal seizures

Neonatal seizures

Severe myoclonic epilepsy of infancy

Epilepsy with continuous spike-waves during sleep

Acquired epileptic aphasia (Landau-Kleffner syndrome)

Other undetermined epilepsies not defined above

3.2. Without unequivocal generalized or focal features (e.g. many cases of sleep-grand

mal)

4. Special syndromes

4.1 Situation-related seizures (Gelegenheitsanfälle)

Febrile convulsions

Isolated seizures or isolated status epilepticus

Seizures due to acute metabolic or toxic factors such as alcohol, drugs, eclampsia

2.1.5 Diagnosis

Since a variety of conditions can cause episodes of transiently disturbed

consciousness or function, the identification of an epileptic seizure is important. If

a patient has had one unprovoked seizure with epileptiform discharges in

electroencephalography (EEG) or two or more unprovoked seizures, the diagnosis

of epilepsy can be made. The diagnosis of epilepsy is based on medical history

and a detailed description of events that occurred before, during and after a

suspected epileptic seizure. Eyewitness information is often essential. Detailed

clinical examination, focusing on neurological and cardiovascular evaluation is

needed, although the clinical examination does not often reveal abnormal findings

in cases of new onset epilepsy. Laboratory tests add little to the diagnostics of

epilepsy. EEG recording is usually conducted to detect possible spike or sharp

wave discharges. Electrocardiography (ECG) is obtained to exclude cardiac

abnormalities. Cerebral computerized tomography (CT) or magnetic resonance

imaging (MRI) scan is done to visualize possible focal structural pathology in the

brain. Because the examinations often yield normal results, the recognition and

25

diagnosis of an epileptic seizure is almost entirely based on medical history, and a

detailed description of the clinical features of the seizure is essential for the

diagnosis. (Shorvon et al. 2004, Elger & Schmidt 2008)

2.1.6 Prognosis

The prognosis of epilepsy varies greatly depending on the aetiology and type of

epileptic syndrome but the overall prognosis for patients with epilepsy is good in

terms of seizure remission. There are differences among published studies

regarding definitions of remission, duration of the follow-up, and the number of

seizures the patients have experienced, which makes comparison of different

studies difficult.

In epilepsy, three prognostic groups are generally considered: (1) spontaneous

remission (20–30% of the over all epilepsy patient population) as seen e.g. in

benign epilepsy with centrotemporal spikes or childhood absences; (2) seizure

remission achieved with AEDs (20–30%), which occurs in most focal epilepsies

and juvenile myoclonic epilepsy syndromes; (3) persistent clinical seizures

despite AEDs (30–40%), i.e. refractory epilepsy. (Kwan & Sander 2004)

Despite an overall good prognosis for seizure control, epilepsy is a potentially

life-threatening condition associated with increased mortality (Cockerell et al.

1997). In population-based studies, the mortality rates have been approximately

2–3 times higher in patients with epilepsy than in the general population

(Olafsson et al. 1998, Tomson 2000). Increased mortality rates are due to deaths

that are unrelated to epilepsy, e.g. ischaemic heart disease, neoplasm outside the

CNS, and to deaths in which epilepsy itself is the cause of death, e.g., suicides,

accidents and status epilepticus. (Hauser et al. 1980, Cockerell et al. 1994)

SUDEP is the most important epilepsy-related cause of death, reported to be

responsible for 18–25% of deaths among patients with medically-refractory

epilepsy (Walczak et al. 2001). SUDEP is discussed in detail in section 2.6.

2.2 Temporal lobe epilepsy

TLE is the most common epileptic syndrome. Anatomically TLE can be

subclassified according to seizure origin to those with seizures originating in the

mesial temporal structures (MTLE) and those with seizures beginning elsewhere

in the temporal lobe (e.g. neocortical temporal lobe epilepsy). (Shorvon et al.

2004)

26

In MTLE seizures originate from the limbic areas of the mesial temporal lobe

such as the hippocampus, amygdala and the parahippocampal gyrus, which are

the most epileptogenic regions of the brain. The main pathology associated with

MTLE is hippocampal sclerosis, which may result from previous status

epilepticus, complicated febrile convulsions, encephalitis or ischaemic insult

(Heinemann 2004). However, other mesial temporal pathologies, e.g. tumours and

congenital pathologies, may also result in MTLE. Recent studies have also

implicated a strong genetic role in the development of hippocampal sclerosis

(Kobayashi et al. 2001).

In MTLE, the seizures usually start in early childhood (Janszky et al. 2004),

but there may be a long seizure-free period from the primary insult before

unprovoked seizures develop. The seizures often respond well to AEDs at the

beginning, but when the seizures return in adolescence they become refractory to

medical treatment (Berg 2008). It has been widely accepted that there is a strong

correlation between hippocampal sclerosis and the severity of epilepsy. Patients

with TLE who have hippocampal sclerosis will become seizure-free in only 10–

30% of the cases with the use of adequate AED treatment, and these patients

should therefore be considered for surgical evaluation. (Shorvon et al. 2004)

2.3 Treatment of epilepsy

2.3.1 Antiepileptic drugs

The treatment of epilepsy is mainly based on drug treatment. In recent decades,

several new AEDs have been developed, and there are currently more than 20

AEDs available, making it challenging for physicians to master the optimal use of

these agents (Prunetti & Perucca 2011).

The goal of the treatment with AEDs is to achieve complete seizure freedom

with as few side effects as possible (Duncan et al. 2006). The treatment is usually

started after two or more unprovoked epileptic seizures, but may also be

considered in patients after a single seizure if specific prognostic factors indicate

a high risk of recurrence, e.g. in patients with an underlying brain disorder, when

the EEG shows interictal epileptiform abnormalities, or in patients who have a

high-risk epilepsy syndrome, such as juvenile myoclonic epilepsy. (Kälviäinen &

Keränen 2001, Shorvon et al. 2004, Brodie 2005) The decision on whether

treatment is appropriate requires careful consideration of the individual risk-

benefit ratio related to treatment, based on such issues as the type and frequency

27

of seizures, type of epilepsy syndrome, age and sex of the patient, presence of

associated medical conditions and the possible side effects of the AED chosen.

(Adult Epilepsy:Current Care summary,2008) In a few specific epilepsy

syndromes, such as benign Rolandic epilepsy, pharmacotherapy may be

unneccessary (Brodie 2005).

Monotherapy is preferred, and about 60–70% of patients with recent onset

epilepsy respond to AED treatment (Cockerell et al. 1995, Kwan & Brodie 2001).

Those who do not experience satisfactory seizure control with monotherapy often

require polytherapy (combination of two or more AEDs). If the seizures continue

at the maximally tolerated dose of the first appropriately chosen AED, and no

underlying aetiology for the seizures is identified, it may indicate refractoriness of

epilepsy (Kwan & Brodie 2000). Refractory epilepsy is usually defined as a

scenario where a patient continues to have seizures despite adequate use of two

well-tolerated AEDs (Kwan et al. 2010). There is currently no evidence to suggest

whether switching to monotherapy with another AED or adding another AED is

more effective in the treatment of seizures in subjects who fail their AED (Beghi

et al. 2003). It has been suggested that combining drugs with different

mechanisms of action is beneficial (Brodie 2005). However, the classification of a

patient’s epilepsy as drug resistant at a given point in time is valid only at the time

of assessment and does not necessarily imply that the patient will never become

seizure-free on further manipulation of AED therapy (Callaghan et al. 2007,

Schiller & Najjar 2008). Since many epilepsies are prone to undergo remission,

the possibility of discontinuation of the AED should be considered after 3–5

years’ seizure freedom (Schmidt & Gram 1996).

Combining AEDs requires an understanding of their pharmacology, in

particular their mechanism of action (Rogawski 2002, Elger & Schmidt 2008).

Although, the mechanisms of action of all AEDs are not fully understood, they

fall into a number of general categories. The molecular targets and clinical

efficacy of different AEDs are presented in Table 3.

28

Table 3. Molecular targets and the spectrum of clinical efficacy of antiepileptic drugs.

Drug Na+

Channels

Ca2+

Channels

GABAA

Receptor

GABA

Transaminase

GABA

Transporter

GABAB

Receptor

NMDA

Receptor

Clinical

Efficacy

Carbamazepine + Partial,

GTC

Oxcarbazepine + Partial,

GTC

Lacosamide + Partial

Phenytoin + Partial,

GTC

Lamotrigine + + Broad

Spectrumb

Zonisamidea + + Partial,

GTC,

myoclonicb

Ethosuximide + + Absence

Phenobarbital + + Broad

Spectrum

Benzodiazepines + Broad

spectrum

Vigabatrin + Partialb

Tiagabine + Partial

Gabapentin + + Partial

Felbamate + + + Broad

spectrum

Topiramatea + + Broad

spectrumb

Valproic acid + + + Broad

spectrum

Levetirecetam + Partial

GTC

aZonisamide and topimarate are weak carbonic anhydrase inhibitors bVigabatrin, lamotrigine, zonisamide and topimarate may be useful in treating infantile spasm

GTC Generalized tonic-clonic

Modified from Rogawski 2002 and Elger & Schmidt 2008

Sodium channel blockers

Carbamazepine (CBZ) is first-line or adjunctive therapy in partial seizures with

or without secondary generalization and is not recommended in the absence or

myoclonic seizures. CBZ is generally well tolerated but CNS side effects are

29

fairly common when the serum concentration is high. CBZ is highly (70–80%)

bound to serum proteins and metabolized almost entirely by the liver, and induces

the cytochrome P450 enzyme system in the liver, which may increase or decrease

the metabolism of other drugs, including other AEDs. (Brodie 1992, Sillanpää

2004) The main mechanism of action of CBZ is on neuronal sodium channel

conductance by reducing high-frequency repetitive firing of action potentials

(Sillanpää 2004), and CBZ slows the conduction velocity of both central and

peripheral nerves (Traccis et al. 1983, Mervaala et al. 1987). CBZ also blocks N-

acetyl-D-aspartate receptors (MacDonald 2002).

Due to the mechanism of action, CBZ is associated with a delay in

atrioventricular conduction and may induce brady-arrhythmias (Ladefoged &

Mogelvang 1982, Boesen et al. 1983, Kasarskis et al. 1992), although sinus

tachycardia has also been observed (Kasarskis et al. 1992). However, there is

evidence that CBZ, at therapeutic levels, has no or minimal effects on the heart

conduction system in the vast majority of patients (Kennebäck et al. 1992). In

addition, CBZ has been shown to increase the sympathetic tone in the autonomic

nervous system and to suppress both parasympathetic and sympathetic function

(Isojärvi et al. 1998). CBZ treatment has also been suggested to be associated

with an increased risk of SUDEP, but the findings are controversial (Kennebäck et

al. 1997, Timmings 1998, Nilsson et al. 1999, Walczak et al. 2001, Hesdorffer et

al. 2011).

Oxcarbazepine (OXC) is a 10-keto analogue of CBZ and its active

monohydroxy derivative limits the firing of sodium-dependent action potentials at

lower concentrations than CBZ. OXC has less potential for drug interactions than

CBZ since it is not metabolized by cytochrome P450-dependent enzymes. OXC is

used as monotherapy or adjunctive therapy in the treatment of partial seizures

with or without generalization. (Faught 2004) The most common side effects

early in the treatment that often lead to discontinuation of OXC affect the CNS.

These side effects include headache, dizziness, ataxia, and nausea (Faught 2004).

Furthermore, hyponatraemia is a common side effect of OXC therapy, especially

in the elderly and female patients (Pendlebury et al. 1989, Isojärvi et al. 2001).

Lamotrigine (LTG) acts by blocking voltage-dependent sodium and calcium

channels in the neural membranes of the brain, thus reducing the epileptic activity

(Meldrum 1996). LTG also inhibits gamma-amino butyric acid (GABA) release.

Furthermore, LTG has been shown to inhibit the cardiac rapid delayed rectifier

potassium ion current (Ikr) (Danielsson et al. 2005). Ikrblocking drugs are

30

generally considered to be associated with an increased risk of cardiac arrhythmia

and SUDEP (Danielsson et al. 2005).

LTG is indicated as an adjunctive or monotherapy in partial and generalized

epilepsies as well as in Lennox-Gastaut syndrome. Headache, skin rash and

nausea are common side-effects of LTG (Brodie et al. 1995). No ECG

abnormalities have been associated with the use of LTG (Betts et al. 1991).

Phenytoin (PHT) is used for partial and tonic-clonic seizures especially when

the seizures are generalized. PHT, as i.v. formulation, is also used to treat status

epilepticus. The mechanism of action is thought to be based on the drug’s

capacity to bind to and prolong the inactivation of voltage-dependent sodium

channels in neuronal cell membranes. (Eadie 2004) PHT has a large number of

interactions with AEDs and other drugs. PHT is highly protein bound (90%) and

metabolized by hepatic P450 enzymes, which may increase or decrease the

metabolism of other drugs, including other AEDs. PHT also has antiarrhythmic

properties and it depresses the hyperactivity of cardiac sympathetic nerves. (Eadie

2004) The main adverse cardiac effect of PHT reported is bradyarrhythmias,

although mostly in i.v. administration of the drug (Earnest et al. 1983). There is

also a case report of complete atrioventricular block with ventricular asystole in a

patient receiving i.v. PHT (Randazzo et al. 1995). The main advantage of PHT

over other AEDs is good efficacy with low cost, but long-term side effects make

it less attractive as a long-term treatment for the majority of epilepsy patients.

Gabaergic drugs

Tiagabine (TGB) is a GABA uptake inhibitor and it prolongs the duration of the

peak inhibitory postsynaptic current, consistent with temporarily sustained levels

of endogenously released GABA in the synapse. TGB has proven effective as

add-on therapy in patients with refractory partial seizures with or without

secondary generalization. (Schachter 1999)

Vigabatrin (VGB) is used as adjunctive therapy in partial and secondarily

generalized epilepsy (Dichter & Brodie 1996), and also for infantile spasms and

Lennox-Gastaut syndrome (Appleton et al. 1999). VGB inhibits presynaptic

GABA degradation by selective, enzyme-activated irreversible blockade of the

mitochondrial enzyme GABA transaminase. VGB is not widely used because its

use is associated with persistent peripheral visual field defects (Kälviäinen et al.

1999).

31

Drugs with other mechanisms of action

Ethosuximide is the first-line or adjunctive therapy in generalized absence

seizures. The mechanism of action against absence seizures is the reduction of

low threshold T-type calcium currents in thalamic neurons. (MacDonald & Kelly

1995)

Gabapentin (GBP) is used as monotherapy in adults with partial or

secondarily generalized epilepsy. It is well tolerated and does not have any

significant drug interactions, and is therefore, is easy to use. GBP is thought to

inhibit high voltage-activated calcium channels (α2δ subunit) (Elger & Schmidt

2008).

Valproate (VPA) differs structurally from other AEDs and its mechanism of

action has remained undefined until now. Different studies have suggested that

VPA increases GABA concentrations through the activation of the GABA-

synthesizing enzyme glutamic acid decarboxylase. However, VPA also blocks

voltage-dependent sodium channels and affects calcium (T) conductance. (Arroyo

2004) VPA is the drug of choice for primary generalized epilepsies and useful in a

wide spectrum of other epilepsies (Perucca 2002). VPA has been shown to be

associated with obesity and hyperinsulinaemia, which may promote

hyperandrogenism, anovulatory cycles, amenorrhoea and polycystic ovary

syndrome that are well-known side effects of VPA (Isojärvi et al. 1993, Rättyä et

al. 1999, Mikkonen et al. 2004).

Topiramate (TPM) is a broad spectrum AED with multiple mechanisms of

action. It enhances GABA action, but it also inhibits sodium conduction, AMPA

subtype glutamate receptors and L-type high-voltage-activated calcium channels.

TPM is used in partial and generalized epilepsies as adjunctive therapy and as

monotherapy if epilepsy is r efractory to commonly used AEDs. Its use is limited

by cognitive side effects. (Glauser 1999)

Levetiracetam (LEV) is well tolerated and effective against partial and

generalized seizures. The exact mechanism of action is not fully confirmed, but it

is thought that LEV binds to a synaptic vesicle protein, SV2A, and acts by

modulating its function (Elger & Schmidt 2008).

2.3.2 Surgery

Epilepsy surgery is defined as any neurosurgical intervention with the primary

goal of eliminating epileptic seizures. About 30 per cent of patients do not

32

achieve seizure freedom despite adequate AED treatment (Kwan & Brodie 2001,

Kwan & Sperling 2009), and for these patients epilepsy surgery may be a

therapeutic alternative. Recently, drug-refractory epilepsy has been defined as the

failure of adequate trials of two well-tolerated and appropriately chosen and used

AEDs to achieve sustained freedom from seizures (Kwan & Sperling 2009).

Trials can usually be completed during 2–3 years (Shorvon & Luciano 2007). If

epilepsy is not controlled within this time interval, it is unlikely to ever be

completely controlled with AEDs alone (Kwan & Brodie 2000) and the

evaluation of surgery is appropriate at that time.

Resective epilepsy surgery can be a curative therapy when the epileptic zone

can be identified, and results in complete seizure control in about 60–80% of this

type of patients with intractable epilepsy (Wiebe et al. 2001, Jutila et al. 2002,

Spencer & Huh 2008). Candidates for epilepsy surgery need to go through a

careful comprehensive pre-surgical evaluation.

2.3.3 Vagus nerve stimulation

General aspects

VNS is a non-pharmacological antiepileptic therapy for patients with refractory

seizures over the age of 12 years who are not candidates for resective surgery or

who have had resective surgery with unsatisfactory results (Uthman et al. 1993,

Ben-Menachem 2002). Absolute contraindications for implantation of VNS are

limited to previous left or bilateral cervical vagotomy. Since the approval of the

VNS Therapy System™ (Cyberonics Inc.) by the Food and Drug Administration

in the US in 1997, over 20,000 patients with epilepsy have been treated with VNS

therapy worldwide (Schachter 2006).

VNS is normally implanted below the left clavicle and the stimulating

electrodes are placed around the left vagus nerve distal to the branching of the

recurrent laryngeal nerve, thereby conveying the electrical signal produced by the

generator to the vagus nerve. The bipolar lead has two connector pins at one end,

which are plugged into the generator, and two separate helical silicone coils at the

other end. Each helix has three turns, with a platinum ribbon electrode within the

middle turn. (Schachter 2004) The implantation procedure is done with a

standardized methodology (Reid 1990).

The generator is individually programmed to stimulate the nerve

automatically. The output current, pulse width and frequency and the duration of

33

each stimulus can be adjusted according to the patient’s needs. In addition, extra

stimulation can be activated at pre-programmed settings through a magnet passed

over the generator in case of aura or a seizure. When the magnet is left in place

over the generator, the device is inactivated. This can be used to suspend

stimulation at times when side effects would be inconvenient. (Schachter 2004)

The anatomy of the vagus nerve is discussed detail in section 2.4.1.

Mechanism of action

Despite extensive experimental studies and some human data, the precise

mechanism of action of VNS is unknown. However, in recent years much

progress has been made through neurophysiological, neuroanatomical,

neurochemical and cerebral blood flow studies in understanding the underlying

mechanisms of action of VNS. In early animal studies VNS stimulation was

shown to induce increased EEG synchronization in non-epileptic animals,

depending on the frequency of stimulation (Zanchetti et al. 1952, Chase et al.

1967). The frequencies and output currents used in different studies vary

(Woodbury & Woodbury 1990, Zabara 1992). Naritoku et al. were the first to

identify some key structures in the neuronal network between the brainstem and

forebrain during VNS stimulation (Naritoku et al. 1995). They showed that VNS

alters multiregional neuronal activities of the brainstem and cortex, especially the

amygdala, a highly epileptogenic region that also plays a role in the

generalization of seizures. Another study showed that an increase in GABA or a

decrease of glutamate transmission in rats’ nucleus tractus solitarius (NTS)

reduces the severity of limbic seizures and provides a potential mechanism for the

seizure protection obtained with vagal stimulation (Walker et al. 1999).

Furthermore, in electrically kindled cats, a model for chronic epilepsy, it has been

shown that VNS delays the development of seizures induced by electrical

kindling in the amygdala suggesting a possible preventative effect of VNS on

epileptogenesis (Fernández-Guardiola et al. 1999).

In human studies, VNS has been observed to increase cerebral blood flow in

the brain, e.g. the thalamus, the right posterior temporal cortex, the left putamen

and the left inferior cerebellum (Ko et al. 1996, Henry et al. 1999). The increased

blood flow in the thalamus has been shown to have significant correlation with

long-term seizure control (Henry et al. 1999). Furthermore, VNS is thought to

affect A and B myelinated fibres, which may contribute to the antiseizure effect

(Handforth et al. 1998, Banzett et al. 1999, DeGiorgio et al. 2000).

34

Chronic VNS also appears to have an effect on various amino acids pools in

the brain. A cerebrospinal fluid study showed a significant increase in GABA

after 3 to 4 months of VNS (Ben-Menachem et al. 1995), which may be related to

the anti-seizure effect of VNS.

Efficacy, safety and tolerability of VNS

VNS treatment has been shown to be safe, well tolerated and effective in seizure

reduction (Amar et al. 1999, Morris, 3rd & Mueller 1999, DeGiorgio et al. 2000,

Schachter 2004, Wheeler et al. 2011) Furthermore, long-term follow-up studies

have shown improved seizure control over time (DeGiorgio et al. 2000, Ben-

Menachem 2002, Uthman et al. 2004). However, even after long-term treatment,

up to 25% of patients do not experience any positive effect of VNS (Ben-

Menachem 2002).

The most frequently encountered adverse effects are stimulation-related, such

as throat pain, coughing, and hoarseness, which are usually mild to moderate in

severity and resolve with reduction of the intensity of the current or

spontaneously over time (Handforth et al. 1998, Schachter & Saper 1998, Morris,

3rd & Mueller 1999, Boon et al. 2001). Lack of the typical CNS side effects seen

with most of the commonly used AEDs is one of the advantages of VNS

treatment compared to AEDs.

2.4 Autonomic nervous system

2.4.1 Anatomy of the autonomic nervous system

ANS is responsible for visceral functions by a complex reflectory system that

provides an effective mechanism in maintaining homeostasis and adapting to the

demands of changing external and internal conditions. Therefore, reactions of

ANS are linked to almost all the physiological and pathological conditions of the

human body, for example cardiovascular, gastrointestinal, urinary, sexual and

thermal functions. (Appenzeller 1990, Loewy 1990b)

The ANS is anatomically and functionally divided into three distinct

interacting divisions: the sympathetic, parasympathetic and entric nervous

systems (Appenzeller 1990, Iversen et al. 2000). The sympathetic and

parasympathetic nervous systems maintain balance in the tonic activities of many

visceral structures and organs (Appenzeller 1990), while the entric nervous

35

system in the wall of the gastrointestinal tract is responsible for the reflex activity

involved in peristalsis and segmentation during the passage of food through the

bowel (Jänig & McLachlan 1999). Both sympathetic and parasympathetic

nervous systems consist of a chain of two neurons, which are separated from each

other by the ganglio dividing the chain into pre- and postganglionic parts.

Acetylcholine is the neurotransmitter for both sympathetic and parasympathetic

preganglionic neurons as well as postganglionic parasympathetic neurons.

Sympathetic postganglionic neurons use noradrenaline as neurotransmitter, with

the exception of the neurons innervating sweat glands, which use acetylcholine.

(Loewy 1990b)

The sympathetic preganglionic neuron cell bodies lie in the spinal cord to

form the intermediolateral cell column of the thoracic and upper lumbar spinal

cord (T1-L4). The preganglionic neurons synapse with the paravertebral ganglio

located laterally to the spinal cord, called truncus sympathicus. The

parasympathetic preganglionic neurons arise either from nuclei in the brain steam

or from the intermediolateral cell column of the sacral spinal cord (S1-S3). They

leave the CNS via distinct cranial nerves, sacral ventral roots and pelvic

splanchnic nerves, projecting their axons directly to the organs they supply. The

postganglionic neurons are located in small ganglia just outside or even within the

wall of the target organ. (Loewy 1990b)

The vagus nerve is the main parasympathetic efferent nerve regulating

autonomic functions. It provides parasympathetic control of the heart, smooth

muscle (pharynx, oesophagus, larynx), glands of the viscera of the neck, the lungs

(bronchial constriction and pulmonary secretion), and the gastrointestinal system

(increased peristalsis and secretions). However, the vagus nerve is also a mixed

nerve composed of about 80% of afferent sensory fibres carrying information

arising from the head, neck and abdomen to the brain (Loewy 1990b). Somata of

the efferent fibres are located in the dorsal motor nucleus and nucleus ambicuus.

Afferent fibres have their origin in the nodose ganglion and primarily project to

the nucleus of the solitary tract (NTS). (Boon et al. 2001)

2.4.2 Cardiovascular regulation

Under normal conditions, the sinus node is the cardiac pacemaker. Although, the

heart possesses an inherited ability for spontaneous, rhythmic initiation of the

cardiac excitation impulse, sinus node activity is also regulated by the ANS

whereas the autonomic activity is regulated in the brainstem where the integration

36

of information from higher cortical centres and the periphery is analysed.

(Benarroch E.E. 1997)

The balance of parasympathetic and sympathetic influences is critical for

control of cardiac function, including HR, excitability and contractility. It is

known that sympathetic nerve fibres innervate the entire heart, including the sinus

node, atrioventricular conduction pathways and the arterial and ventricular

myocardium, while the vagus nerve innervates the sinus node, the atrioventricular

pathways and the atrial muscle (Kamath & Fallen 1993). Parasympathetic

activation decelerates the HR where as sympathetic activation increases it.

Furthermore, HR is mostly influenced by the right vagus nerve that has dense

projections primarily to the atria of the heart.

The central autonomic network controls autonomic functions in a tonic,

reflexive and adaptive manner and integrates autonomic with hormonal,

behavioural, immunomodulatory and pain-controlling responses to internal or

external environmental challenges. The central autonomic network is composed

of several interconnected areas distributed throughout the neuraxis including

central nucleus of amygdala, several nuclei of the hypothalamus and NTS.

(Benarroch E.E. 1997)

NTS is the major visceral sensory relay cell group in the brain and it receives

inputs from all the major organs of the body. Afferents from cardiac receptors,

pulmonary receptors, and gastrointestinal receptors project to specific areas in the

NTS. Furthermore, there are specific areas in the NTS to gain information from

the carotid sinus and aortic depressor nerves that transmit high-pressure

baroreceptor and chemoreceptor afferent information. The information is

processed in the NTS and used to affect a number of autonomic, neuroendocrine

and behavioural functions. (Loewy 1990a)

The NTS has widespread projections to numerous areas in the forebrain as

well as the brainstem including important areas for epileptogenesis such as the

amygdala and the thalamus. There are direct neural projections into the raphe

nucleus, which is the major source of serotonergic neurons and A5 nuclei that

contain noradrenergic neurons. Furthermore, there are numerous diffuse cortical

connections. (Rutecki 1990, McLachlan 1993)

Cardiovagal motoneurons are located in the nucleus ambiguus and dorsal

vagal nucleus (Kalia 1981) and both regions receive inputs from the NTS, the site

of termination of cardiovascular and respiratory afferents involved in

cardiorespiratory reflexes (Benarroch E.E. 1997). Nucleus ambicuus and dorsal

37

vagal nucleus also receive projections from the hypothalamus, amygdala and

insular cortex.

Hippocampal structures, especially the amygdala, are among the centres at

the highest level of cardiovascular autonomic control (Frysinger & Harper 1990).

Central nucleus of the amygdala receives inputs from the NTS and from the

parabrachial nucleus, and a number of other areas and fibres from the amygdala

project to the hypothalamic area, medial parabrachial nuclei, locus coeruleus and

raphe nuclei and to the NTS and to the dorsal motor nucleus of the vagus nerve,

being thus directly involved in the autonomic modulation of HR.

38

Fig. 1. Graph illustrating autonomic cardiovascular regulation. AV=atrioventricular

node, CAN=central autonomic network, DVN=dorsal vagal nucleus, NA=nucleus

ambiguous, NTS=nucleus tractus solitarius, SN=sinus node. (Modified after Loewy

1990a and Benarroch 1997).

NTS

Gastrointestinal Receptors

Cardiovascular Receptors

Respiratory Receptors

PulmonaryReceptors

NA, DVN=cardiovagal motoneuron

Parasympathetic nervous system Sympathetic neurvous system

HEARTSNAV

Central integration

Reflexes

VISCERAL AFFERENTS

CAN

Amygdala

Hypothalamus

HR ↓ HR ↑

39

2.5 Heart rate variability and its clinical implications

2.5.1 Physiological background of heart rate variability and heart rate dynamics

HR variability is a term that is used to describe the variations in beat-to-beat

fluctuations around the mean HR. It gives information about the sympathetic and

parasympathetic autonomic balance and other physiological control mechanisms

on cardiac function. A high variability in HR is a sign of good adaptability,

implying a healthy individual with well-functioning autonomic control

mechanism. (Task Force 1996)

Measurement of HR variability has become a widely used tool for assessing

cardiovascular autonomic function in various physiological and pathological

conditions (Lipsitz et al. 1990, Huikuri et al. 1993, Korpelainen et al. 1996,

Tulppo et al. 1996, Tomson et al. 1998, Ansakorpi et al. 2000, Pikkujämsä et al.

2001). Long-term, usually 24-hour ECG recording can be used to assess

autonomic nervous responses during normal daily activities in healthy subjects,

subjects with disease and in response to therapeutic interventions, e.g. exercise or

drugs. Furthermore, 24-hour ECG recordings are a non-invasive and easy

approach to gain information on cardiovascular function and HR variability, and

the measurements have good reproducibility if used under standardized conditions

(Kleiger et al. 1991).

The physiological mechanisms underlying the various measures of HR

variability differ from each other. The average HR and the standard deviation of

all normal-to-normal RR intervals over an entire recording (SDNN) as the time

domain measures of HR variability have been found to reflect well both

sympathetic and parasympathetic influences on HR variability (Bigger, Jr. et al.

1989, Kleiger et al. 1992). Careful editing to exclude ectopic beats, artifacts and

missed beats is required to calculate SDNN accurately (Kleiger et al. 2005).

Power spectrum analysis reflects the amplitude of HR fluctuations present at

different oscillation frequencies (frequency domain measures of HR variability).

Very low frequency (VLF) is found at frequency 0.005–0.04 Hz. The exact

physiologic mechanism of VLF is not understood in detail, but it is suggested that

VLF power reflects the thermoregulation or vasomotor activity (van

Ravenswaaij-Arts et al. 1993). Furthermore, VLF power is reduced by

angiotensin-converting enzyme inhibition, suggesting that it reflects the activity

of the renin-aldosterone system (Bonaduce et al. 1994).

40

The low frequency (LF) component is observed around 0.04–0.15 Hz and is

modulated by baroreflexes with a combination of sympathetic and

parasympathetic efferent nerve traffic to the sinus node (Kamath & Fallen 1993,

Taylor et al. 1998). An increase in LF power has been proposed as being a marker

of sympathetic activation (Kamath & Fallen 1993), although, the parasympathetic

regulation has been reported to have an influence on the LF power (Akselrod et

al. 1985, Pomeranz et al. 1985).

High frequency (HF) is found around 0.15–0.4 Hz and it reflects ventilatory

modulation of RR intervals (respiratory sinus arrhythmia) with the efferent

impulses on the cardiac vagus nerve, which is considered to be a marker of

parasympathetic activation (Pomeranz et al. 1985, Pagani et al. 1997).

Geometrical methods are techniques in which RR intervals are converted into

various geometrical forms. In Poincaré scatterograms, each RR interval is plotted

as a function of the previous one. The plots can be interpreted either visually or

quantitatively (Huikuri et al. 1996, Tulppo et al. 1996). The standard deviation of

the longitudinal axis of the plot (SD2) is a marker of long-term HR variability,

while the standard deviation of the vertical axis is a marker of short-term beat-to-

beat (SD1) (Huikuri et al. 1996). The former reflects partly physical activity of

the patients in addition to autonomic regulation of HR (Tulppo & Huikuri 2004).

The latter index is a more direct measure of cardiac vagal outflow. In fact, SD1 is

a more reliable index of cardiac vagal activity than the HF spectral component of

HR variability, when measured from ambulatory recordings (Tulppo et al. 1998).

One advantage of the Poincaré method over spectral analysis techniques is that it

is not sensitive to stationary irregularities and trends in RR intervals, therefore

being more suitable for HR variability analyses using ambulatory ECG recordings

(Tulppo et al. 1996).

Physical activity, emotional stimuli or reflexes of various kinds can cause

non-periodic changes to RR interval time series. Newer non-linear dynamic

methods based on chaos system theory have been developed to detect these

changes. These newer methods offer information on the quality properties of HR

fluctuation.

Detrended fractal scaling exponent, also referred to as short-term scaling

exponent α, is computed from detrended fluctuation analysis and is a measure of

the degree to which the RR interval pattern is random at one extreme, or

correlated at the other on a scale of 3–11 beats (Kleiger et al. 2005). Short-term

scaling exponent α, has been shown to predict sudden cardiac death in the random

population of elderly subjects (Mäkikallio et al. 2001). In addition, abnormal

41

short-term scaling measure α has been reported to be associated with life

threatening arrhythmias in patients with myocardial infarction (Mäkikallio et al.

1997, Mäkikallio et al. 1999b).

The slope β of the power-law relationship of HR variability reflects the

distribution of spectral characteristics of RR interval oscillations (Saul et al. 1988,

Bigger, Jr. et al. 1996). The physiological background for the spectral distribution

is not known exactly, but the observation of a significantly steeper slope in

denervated hearts (Bigger, Jr. et al. 1996) suggests that the autonomic nervous

system has an important role in determining long-term HR variability. Previous

studies have shown that altered long-term variability measurements predict

mortality in patients with impaired left ventricular function (Mäkikallio et al.

1999a), after stroke (Mäkikallio et al. 2004) as well as in the elderly (Huikuri et

al. 1998).

Approximate entropy (ApEn) is a measure that quantifies the predictability or

regularity of time series data. A low value indicates that the signal is deterministic

while a high value indicates randomness. In previous studies, physiological

ageing has been associated with a loss of ApEn (Lipsitz & Goldberger 1992,

Pikkujämsä et al. 1999) but the clinical significance has, yet, remained unclear.

2.5.2 Factors affecting heart rate variability

Previous studies with healthy people have shown that HR variability decreases

with normal ageing being lower in elderly people compared to middle-aged or

young subjects, and these age-related changes seem to be modified by gender

(Hayano et al. 1991, Pikkujämsä et al. 1999, Fukusaki et al. 2000, Jokinen et al.

2005). It has been shown that HR variability is lower in women than in men

(Ramaekers et al. 1998, Bonnemeier et al. 2003). The gender difference in HR

variability is most pronounced in subjects younger than 30 years, disappearing

with age by approximately 50 years of age (Umetani et al. 1998, Antelmi et al.

2004).

Many medications act directly or indirectly on the ANS and affect HR

variability measurements. Thus, the influence of medication needs to be taken

into account in the analysis of HR variation. Atropine has been observed to

abolish respiratory sinus arrhythmia (Akselrod et al. 1985). There are also studies

on the effects of drugs on HR variability performed with antiarrhythmic drugs,

anaesthetics, sedatives and chemotherapeutic agents (Task Force 1996). However,

42

further studies are needed to assess possible effect of different drugs on HR

variability.

Smoking and alcohol use reduces HR variability (Malpas et al. 1991, Lucini

et al. 1996) while exercise is shown to increase it (Tulppo et al. 1998, Hautala et

al. 2009).

2.5.3 Heart rate variability in pathological conditions

The analysis of HR variability has been used widely in quantifying risk in both

cardiac and non-cardiac diseases, e.g. stroke, diabetes mellitus, multiple sclerosis,

ischaemic heart disease, cardiomyopathy and congestive heart disease (Kleiger et

al. 1987, Ewing 1991, Bigger, Jr. et al. 1996, Mäkikallio et al. 2004). Reduced

HR variability has been associated with increased cardiac arrhythmogenic

mortality in patients with various heart diseases and overall mortality in the

elderly (Kleiger et al. 1987, Huikuri et al. 1994, Malliani et al. 1994, Mäkikallio

1996). Some studies have also associated low HR variability with sudden cardiac

death (Barron & Viskin 1998). Reduction of HR fluctuation has also been

reported in various neurological diseases, including stroke (Korpelainen et al.

1997), and Parkinson’s disease (Pursiainen et al. 2002), but its clinical

significance in these settings is still undefined.

Analysis based on non-linear dynamics of HR fluctuation seems to provide

prognostic information among patients with various cardiac diseases and reveal

alterations in HR dynamics not detectable with conventional analysis methods

(Peng et al. 1995, Mäkikallio et al. 1999a, Huikuri et al. 2000). One study

showed that altered short-term fractal scaling properties of HR indicate an

increased risk for cardiac mortality, particularly sudden cardiac death, in the

random population of elderly subjects (Mäkikallio et al. 2001). Furthermore, it

has been suggested that abnormal long-term HR dynamics predict post-stroke

mortality (Mäkikallio et al. 2004). Reduction in HR variability has been shown to

be associated with increased risk of mortality in septic patients as well (Garrard et

al. 1993, Buchman et al. 2002). However, there is currently no consensus about

the best available index of HR variability for clinical use or risk stratification.

43

2.6 Epilepsy and autonomic cardiovascular dysregulation

2.6.1 Ictal autonomic dysfunction

Epileptic seizures can provoke a variety of autonomic responses such as

cardiovascular, respiratory, gastrointestinal, cutaneous, urinary, and genital

manifestations, and also emotional and sexual feelings (Devinsky 2004). The

effects of seizure discharges on ANS are thought to be mediated through the

cortical, limbic and hypothalamic systems, and thus the seizures that arise from or

spread to areas in the central autonomic network can mimic stimulation of

autonomic afferents or modify autonomic expression (Goodman et al. 1990,

Oppenheimer et al. 1992).

Episodes of tachycardias are considered to be the most common ECG

changes during seizures (Smith et al. 1989, Leutmezer et al. 2003, Rugg-Gunn et

al. 2004). Bradycardias are thought to be a rare event (Devinsky 2004), and

asystole episodes even less frequent (Schuele et al. 2007). However, it has been

suggested that ictal bradycardias with or without asystoles are currently

underestimated (Nashef et al. 1996, Tinuper et al. 2001, Rugg-Gunn et al. 2004).

According to MRI and CT scan findings, it seems that these ECG findings

can occur in the absence of any obvious structural brain abnormality and the

presence of particular changes in HR does not express side of the seizure focus

(Reeves et al. 1996, Britton et al. 2006). It has also been shown that seizures

arising from the temporal lobe, especially mesial lobe onset seizures, are more

prone to elicit HR changes (Galimberti et al. 1996, Tinuper et al. 2001, Garcia et

al. 2001, Leutmezer et al. 2003, Britton et al. 2006). Given that in TLE seizures

arise near the centres controlling cardiovascular regulation, the high incidence of

HR changes during seizures with TLE does not surprise.

In one study, there was a higher risk of ictal ECG abnormalities when

seizures arose from sleep or from the left hemisphere when MRI showed

evidence of hippocampal sclerosis (Opherk et al. 2002). Furthermore, in patients

with epilepsy different abnormalities in ECG morphologies during or

immediately after seizures have been observed, e.g. ST-segment depression

(Opherk et al. 2002, Tigaran et al. 2003) or elevation (Nei et al. 2000, Nei et al.

2004), life-threatening asystoles (Liedholm & Gudjonsson 1992, Devinsky et al.

1997, Rugg-Gunn et al. 2000, Mascia et al. 2005) and total atrioventricular block

(Tigaran et al. 2002).

44

There have been attempts to identify seizures by analysing HR and HR

variability measurement in patients with epilepsy. Novak et al. found that there

are detectable HR variability changes minutes prior to the clinical onset of

complex partial seizures (Novak et al. 1999). Similarly, other studies have

confirmed these findings that seizures can be predicted using HR or HR

variability analysis (Kerem & Geva 2005, van Elmpt et al. 2006). However, these

methods are not currently in clinical use.

2.6.2 Interictal heart rate variation

During the interictal state the prevalence of cardiac arrhythmias has been noted to

be similar in patients with epilepsy to that in the healthy population (Blumhardt et

al. 1986, Massetani et al. 1997). However, increased interictal HR has been

observed in some patients with various types of epilepsy (Evrengul et al. 2005,

Harnod et al. 2008). These changes in HR have been suggested to occur due to

alteration of autonomic cardiac function.

Using conventional short- and long-term HR variability analysis methods, it

has been shown that patients with chronic epilepsy have dysfunction of both

parasympathetic and sympathetic nervous systems during the interictal state

(Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Tomson et al.

1998, Ansakorpi et al. 2000, Harnod et al. 2008), but the clinical significance of

these findings has not been established. Although the mechanisms leading to such

autonomic dysfunction are not yet clearly understood, it has been suggested that

diminished interictal HR variability in patients with TLE is due to the epileptic

process itself, rather than any specific AED regimen (Ansakorpi et al. 2002), but

the opposite has also been proposed (Devinsky et al. 1994, Tomson et al. 1998).

Overall, it is difficult to differentiate with certainty the influence of epilepsy itself

and that of AEDs.

Previous studies indicate that antiarrhythmic drugs with sodium channel

blocking properties are associated with increased mortality and reduced HR

variability. Regarding the AEDs, CBZ which is also a sodium channel blocker has

most often been suggested to be associated with the altered ANS function

observed in patients with epilepsy (Devinsky et al. 1994, Isojärvi et al. 1998,

Tomson et al. 1998, Ansakorpi et al. 2000, Persson et al. 2003). In addition, one

study in patients with medically intractable seizures reported increased cardiac

sympathetic activity during sleep induced by sudden discontinuation of CBZ

(Hennessy et al. 2001). However, in contrast to previous findings, one study

45

found that sympathetic autonomic dysfunction was less severe in patients using

CBZ or OXC compared with patients not using these drugs (Koseoglu et al.

2009).

In TLE seizures arise from the mesial temporal structures, i.e. the amygdala

and hippocampus, or neocortical regions, and damage in those areas may result in

abnormalities manifested by altered HR variation. This is supported by

observations that hippocampus sclerosis may be associated with decreased

interictal HR variation (Ansakorpi et al. 2004, Koseoglu et al. 2009). One

previous study found that epilepsy surgery does not affect HR variability (Persson

et al. 2006), although HR variability was reduced in epilepsy surgery candidates

before surgery (Persson et al. 2005). Indeed, these autonomic alterations seem not

to exist or are minor in early stages of epilepsy, and evolve with time along with

the epileptic process itself. However, the role of structural brain lesions and

chronic epilepsy in autonomic dysfunction is difficult to assess.

Impaired autonomic cardiac control in patients with epilepsy is of particular

interest considering that reduced HR variability has been shown to predict

mortality and sudden death in other conditions than epilepsy (Binder et al. 1992).

It is also interesting to note that chronic TLE is associated with reduced interictal

HR variability (Massetani et al. 1997, Tomson et al. 1998, Ansakorpi et al. 2002,

Mukherjee et al. 2009), since patients with chronic TLE seems to be at greater

risk for SUDEP. However, there are no prospective studies regarding progression

of changes in HR variation in patients with epilepsy in the long term.

2.6.3 Circadian heart rate variation

In animal studies clear diurnal patterns of seizures have been observed in various

epilepsy models (Quigg et al. 1998). It is also well known that night time seizures

are common in some frontal lobe epilepsies, e.g. autosomal dominant nocturnal

frontal lobe epilepsy, and myoclonic seizures in juvenile myoclonic epilepsy

occur predominantly after awakening in the morning. Furthermore, many

physiological functions, such as thermoregulation, wakefulness and sleep, show

diurnal variation (Hastings et al. 2007), and there is also a significant decrease in

arterial pressure in healthy subjects during sleep (Millar-Craig et al. 1978).

Previous studies have mostly analysed HR variability from full 24-hour ECG

recordings, although it is well established that HR and HR variability have a

circadian rhythm (Lombardi et al. 1992, Huikuri et al. 1994). Reduction of

circadian HR fluctuation has been reported in various cardiovascular and

46

neurological diseases, including stroke (Korpelainen et al. 1997), diabetes

mellitus (Bernardi et al. 1992), coronary artery disease (Huikuri et al. 1994),

hypertension (Chakko et al. 1993) and Parkinson’s disease (Pursiainen et al.

2002). After an acute myocardial infarct, suppressed circadian fluctuation seems

to be related to lethal arrhythmic events (Huikuri et al. 1992), and infants at risk

of sudden infant death syndrome show a significant reduction in HR variability

during sleep (Harper et al. 1978, Eiselt et al. 1993).

There is one study (Ferri et al. 2002) concerning changes in HR variation

during sleep in children with partial epilepsy. The results showed that during

sleep, patients with epilepsy tended to have an overall lower HR variability in

both time- and frequency-domain parameters, which was most evident for HF

absolute power. Therefore, LF/HF ratio was higher in patients than in normal

controls. In accordance with the previous finding, one study reported that patients

who later died of SUDEP were found to differ from other patients with refractory

epilepsy in that seizures during sleep induced a more pronounced increase in HR

than seizures during wakefulness (Nei et al. 2004). In this regard, one previous

study observed that diminution of circadian HR variation during the night may, in

fact, be a marker for an increased risk for SUDEP (Eppinger et al. 2004), and thus

the relationship between HR variability and altered HR variation during the day

and night time is interesting.

2.6.4 Effect of vagus nerve stimulation on cardiovascular autonomic function

Previous studies have only reported cardiac arrhythmias during lead tests upon

VNS device implantation (Tatum et al. 1999, Ali et al. 2004) However, some

studies have also reported cardiac rhythm changes during chronic VNS treatment

(Frei & Osorio 2001, Amark et al. 2007).

Despite the close interaction between the vagus nerve and the heart, there are

only few studies concerning the effects of VNS on HR variability. The most

important previous studies were performed only during wakefulness and after

short-term VNS treatment. One previous study found a significant increase in HF

component (Kamath et al. 1992) while other studies suggested that VNS does not

affect HR variation (Handforth et al. 1998, Setty et al. 1998). It has been

suggested that the cardiac rhythm does not change with the stimulation of the

vagal nerve during sleep (Murray et al. 2001).

47

Only one study has tried to explore the long-term effects of VNS on cardiac

vagal tone (Galli et al. 2003). In that study, long-term VNS therapy appeared to

have some effects on cardiac autonomic function, with a reduction of the HF

component during the night and a flattening of sympathovagal circadian changes.

During VNS treatment, the left vagus nerve is stimulated below the cardiac

branches of the vagus nerve, and this may explain why the cardiac function is

unaffected by routine VNS treatment. However, further studies are needed to

clarify the effect of VNS on cardiac autonomic control.

2.7 Sudden unexpected death in epilepsy

2.7.1 Definition

There has been a lack of consensus regarding the definition of SUDEP. The most

widely used definition has defined SUDEP as a “sudden, unexpected, witnessed

or unwitnessed, non-traumatic and non-drowning death in a patient with epilepsy,

with or without evidence of a seizure and excluding documented status

epilepticus, in which postmortem examination does not reveal a toxicologic or

anatomic cause of death”. (Nashef 1997) Due to differences in SUDEP

definitions and methodologies it is challenging to compare the results of different

studies on SUDEP.

2.7.2 Epidemiology

The risk of SUDEP is increased in the general epilepsy population, but the

reported incidence varies widely depending on criteria and definitions, study

methods, and in particular on the type of epilepsy population under study.

According to community-based studies, the incidence of SUDEP ranges from

0.09 to 2.3 per 1,000 person-years (Terrence, Jr. et al. 1975, Leestma et al. 1989,

Ficker et al. 1998, Langan et al. 1998, Lhatoo et al. 2001). Furthermore, SUDEP

incidence appears to increase steadily with increasing severity of epilepsy, from

1.5–5.9 per 1,000 person-years in refractory epilepsy cohort studies (Nashef et al.

1995, Timmings 1998, Walczak et al. 2001) up to 6.3–9.3 per 1,000 person-years

in epilepsy surgery group or patients who continued to have seizures after surgery

(Dasheiff 1991, Hennessy et al. 1999, Nilsson et al. 2003, Sperling et al. 2005).

However, it has to be remembered that also patients with newly diagnosed

epilepsy and patients whose epilepsy is in remission are at risk for SUDEP,

48

although the risk is lower (Harvey et al. 1993, Racoosin et al. 2001, Langan et al.

2005). In children the SUDEP rates are thought to be lower than in adult

population, varying between 0.11–0.43 per 1,000 person-years (Harvey et al.

1993, Camfield et al. 2002, Weber et al. 2005). However, according to a recent

study from a cohort of subjects with childhood-onset epilepsy the risk of SUDEP

was noted to be higher compared to previous studies (Sillanpää & Shinnar 2010)

There are currently only a few studies on the rates of SUDEP in patients with

VNS treatment. Annegers et al. found the rates of SUDEP and mortality in

patients with VNS treatment to be in accordance with patients with intractable

epilepsy of young adult age (Annegers et al. 1998). After two years’ follow-up

the rate of SUDEP seems to be lower in the same cohort of patients with VNS,

which may be due to improved seizure control (Annegers et al. 2000).

2.7.3 Aetiology

SUDEP was already recognized in the middle of the 19th century. During recent

decades the interest in SUDEP and SUDEP-related research has increased

significantly. The aetiology of SUDEP has remained elusive, but some risk factors

have been identified. Uncontrolled descriptive studies have identified a risk

profile: youth, male sex, chronic alcohol use, lack of compliance with treatment

(Leestma et al. 1989, Devinsky et al. 1994, Opeskin et al. 2000, Tellez-Zenteno et

al. 2005). Studies using living patients with epilepsy as controls have proposed

poor seizure control, polytherapy with AEDs, onset of epilepsy at young age and

long duration of epilepsy as risk factors for SUDEP (Nilsson et al. 1999, Walczak

et al. 2001, Langan et al. 2005, Hitiris et al. 2007). Furthermore, one recent study

showed that in childhood onset epilepsy the risk of SUDEP was especially high

among patients with active epilepsy (Sillanpää & Shinnar 2010). However,

otherwise healthy, compliant patients may also die unexpectedly (Earnest et al.

1992, Nashef et al. 1998).

Abnormalities in autonomic cardiovascular regulation and its connection with

SUDEP have been debated. It has been suggested that SUDEP may be caused by

dysfunction of the cardiovascular autonomic control, which exposes the patient to

cardiac arrhythmias, sinus arrest and neurogenic pulmonary oedema (Schraeder &

Lathers 1989, Nashef et al. 1996, Nashef et al. 1998, So et al. 2000). In

accordance with this, one study showed that complex partial, tonic, or generalized

tonic-clonic seizures caused central apnoea of significant duration in patients with

epilepsy who were on video EEG monitoring (Nashef et al. 1996). However,

49

apnoea might also represent only ictal symptom of temporal lobe seizures (Lee et

al. 1999). Recent studies have also proposed a genetic ion channel dysfunction

predisposing to the development of cardiac arrhythmia and possibly to SUDEP

(Tester et al. 2005, Tu et al. 2011).

Circumstances of death in SUDEP cases are noted to be remarkably uniform

in various studies. Patients are often found in bed or are known to have been

asleep before death (Leestma et al. 1989, Nashef et al. 1996, Kloster &

Engelskjøn 1999, Nei et al. 2004, Nobili et al. 2010) suggesting that the risk for

specific aetiologic mechanisms directly responsible for death may increase during

sleep and sleep-related seizures could differ pathophysiologically. Deaths

following a recent seizure are supported by evidence of acute neuronal injury in

the hippocampus in some patients with SUDEP (Thom et al. 2003).

The role of different AEDs in SUDEP has remained controversial. A large

case control study showed that compared to having been on one or two drugs,

absence of treatment with AEDs was a strong risk factor for SUDEP (Langan et

al. 2005). Similarly, some studies report non-compliance with AEDs to be a risk

factor (George & Davis 1998, Williams et al. 2006). There are few studies that

have tried to analyse whether there is an association between specific AEDs and

increased incidence of SUDEP. One study found an association between SUDEP

and CBZ treatment (Timmings 1998). In contrast, another study found no

difference between monotherapy with CBZ or phenytoin in relation to SUDEP

incidence (Nilsson et al. 1999). Moreover, LTG has also been suggested to be

associated with increased risk for SUDEP (Aurlien et al. 2007, Hesdorffer et al.

2011). Overall, the association between different AEDs and SUDEP is

challenging. AEDs might prevent SUDEP by improving seizure control.

Conversely, abrupt AED withdrawal can considerably increase sympathetic tone

during sleep and the occurrence of adverse cardiac arrhythmias (Kennebäck et al.

1997, Hennessy et al. 2001). However, there is currently no well-defined reason

to avoid any particular AED to reduce the risk of SUDEP (Walczak 2003).

The results from previous studies concerning epilepsy surgery and its

possible association with SUDEP are conflicting. Some studies indicate that

patients who remained seizure-free after resective temporal lobe surgery had

lower SUDEP rates compared to those who continued to have seizures

postoperatively (Sperling et al. 1999, Salanova et al. 2002). Findings regarding

whether there are differences in SUDEP rates between patients with drug resistant

epilepsy after surgery compared to those who were medically treated vary a lot

(Vickrey et al. 1995, Nilsson et al. 2003, Stavem & Guldvog 2005).

50

Studies concerning seizure-related changes in cerebrovascular autoregulation

and SUDEP are limited. It has been suggested that an electrical cerebral shut-

down might play a role in the mechanism of SUDEP. This hypothesis is based on

observation in patients with SUDEP cases where EEG activity suddenly started

flattening before the occurrence of any fatal cardiac or respiratory arrest was

shown (McLean & Wimalaratna 2007). It has been postulated that a primary

cause could be an alteration of cerebral blood flow autoregulation leading to a

sudden drop of cerebral perfusion and subsequent cessation of electrical activity

(Surges et al. 2009b).

In conclusion, SUDEP seems most often to be associated with chronic

uncontrolled epilepsy with a wide range of different risk factors mentioned above

(Tomson et al. 2008). Therefore, identification of individual patients at risk of

SUDEP is challenging.

51

3 Aims of the study

The aim of the study was to evaluate autonomic cardiovascular regulation in

patients with TLE and in patients with refractory epilepsy during VNS treatment,

using 24-hour ambulatory ECG recordings. The specific aims of the individual

studies were:

1. To assess HR variation in patients with well-controlled or refractory TLE (II).

2. To evaluate whether HR variation changes with time in patients with chronic

TLE (II).

3. To assess circadian HR dynamics in patients with well-controlled or

refractory TLE (I).

4. To evaluate whether circadian HR dynamics change with time in patients

with chronic TLE (III).

5. To study the effects of VNS therapy on interictal HR variability in patients

with refractory epilepsy (IV).

52

53

4 Subjects and methods

This study was carried out at the Department of Neurology at Oulu University

Hospital, Finland, during the years 1999–2011. The study was approved by the

Ethics Committee of the Northern Ostrobothnia Hospital District, and carried out

according to the principles of the Declaration of Helsinki. All patients and control

subjects gave their informed consent before their inclusion in the study.

4.1 Subjects

This study consisted of three different study populations. The clinical

characteristics of the patients and the control subjects in the individual studies (I-

IV) are presented in Table 4. In Studies I-III consecutive patients with refractory

TLE and well-defined lateralization of the epileptic focus seen at the Department

of Neurology, University Hospital of Oulu, were considered for the study. A

similar number of patients with well-controlled TLE and well-defined

lateralization of the epileptic focus were identified as control subjects.

Thirty-seven patients with TLE participated in Study I. Of these, 17 patients

had refractory TLE and 20 had well-controlled TLE. An interictal EEG recording

was obtained from all the patients. Normal EEG or general slowing was seen in

15 patients. Left temporal focal slow waves, or irritation, or both were detected in

15, whereas right temporal focal abnormalities were seen in 7 patients

Thirty-six patients with TLE participated in Studies II-III. Of these, 18

patients had refractory TLE and 18 had well-controlled TLE.

In Studies I-III patients with manifestations of other disease (e.g. diabetes

mellitus, alcoholism or cardiopulmonary disease), or other central or peripheral

nervous system disorders or medication (besides AEDs) known to affect the ANS

were excluded from the study. Female patients who were pregnant or lactating

were also excluded.

The exclusion criteria were met in male patients more often than female

patients, which resulted in a smaller number of male patients in the study. The

patients were considered to have refractory TLE if their seizures were not

controlled despite appropriate use of AEDs and they had not been considered

suitable candidates for resective epilepsy surgery. Patients were considered not to

be appropriate candidates for resective epilepsy surgery if the MRI was normal, if

the epileptic focus could not be identified, if the patient refused surgery, or if the

patient had psychiatric problems or other contraindications for surgery. The

54

patients were considered to have well-controlled TLE if they were seizure-free or

were experiencing less than two seizure per year.

Fourteen patients with refractory epilepsy seen in the outpatient clinic of the

Department of Neurology, Oulu University Hospital were included in Study IV.

None of these subjects were considered candidates for resective surgery after a

careful presurgical evaluation. A Neurocybernetic Prosthesis (NCP Generator

Cyberonic, Webster, TX, USA) was implanted in all the patients for the treatment

of refractory epilepsy.

The control group consisted of 101 healthy age- and sex-matched subjects

chosen from a group of healthy individuals participating in a study comparing the

characteristics of hypertensive and normotensive subjects who in turn had been

randomly selected by their personal social security numbers from the general

population of Oulu. None of them had medication or diseases affecting the ANS

in their medical history (Studies I, III-IV).

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dC

ontr

ol

Refr

act

ory

W

ell-

contr

olle

d

Contr

ols

Patie

nts

C

ontr

ol

Base

line

Follo

w-u

pB

ase

line

Follo

w-u

pB

ase

line

(n=

17)

(n=

20)

(n=

37)

(n=

18)

(n=

18)

(n=

18

) (n

=1

8)

(n=

36

) (n

=1

4)

(n=

28

)

Age, ye

ars

32.1

±7.2

32.2

±6.6

32.2

±8.3

32.4

±7.1

32.2

±6.6

32.7

±8.5

34.3

±9.3

34.4

±9.3

Male

/fem

ale

4/1

3

9/1

1

13/2

4

4/1

4

9/9

13/2

3

8/6

16/1

2

Dura

tion o

f epile

psy

22.7

±10.0

14.2

±10.1

N

.A.

22.4

±9.8

13.9

±10.4

N

.A.

27.3

±10.8

N.A

.

Se

izu

re-f

ree

(n

o.

of

pa

tien

ts)

0

11

N

.A.

0

1

11

1

6

N.A

. 0

N

.A.

Seiz

ure

s per

month

20.3

±38.2

0.0

3±0.0

7

N.A

. 20.9

±37.2

14.9

±26.8

0.0

2±0.0

5

0.8

±3.5

N

.A.

48.4

±38.3

N.A

.

Antie

pile

ptic

medic

atio

n

Mo

no

the

rap

y:

Ca

rba

ma

zep

ine

(C

BZ

) -

10

-

- 1

1

0

4

- -

-

Oxc

arb

aze

pin

e (

OX

C)

4

7

- 4

-

7

7

- -

-

Ph

en

yto

in (

PH

T)

- 1

-

- -

- -

- -

-

La

mo

trig

ine

(L

TG

) -

1

- -

- 1

2

-

- -

Po

lyth

era

py:

-

CB

Z w

ith o

the

r A

ED

(s)

7

1

- 7

4

-

1

- 6

-

OX

C w

ith o

the

r A

ED

(s)

6

- -

7

9

- -

- 4

-

LT

G w

ith o

the

r A

ED

(s)

- -

- -

4

- -

- 3

-

Oth

er

AE

D c

om

bin

atio

n

- -

- -

- -

- -

1

-

No

me

dic

atio

n

- -

- -

- -

4

- -

-

Abbre

viatio

ns:

The v

alu

es

are

means

± s

tandard

devi

atio

n. N

.A. =

not applic

able

.

55

56

4.2 Methods

4.2.1 Clinical examination (Studies I-IV)

All the patients were carefully interviewed and clinically examined. Their

epilepsy and seizure type was classified according to the recommendations of the

International League Against Epilepsy (ILAE 1981,1989). An interictal EEG

recording was obtained from all the patients.

In the case histories obtained with a structured interview, special attention

was given to the following subjective symptoms reflecting possible autonomic

dysfunction: cardiac arrhythmias, dizziness due to orthostatic hypotension,

changes in sweating, and sexual malfunction. Laboratory screening (liver and

renal functions, serum electrolytes and basic haematological indices) was in

general normal in all the study patients. Blood samples for the laboratory tests

were taken in the morning after the subjects had taken the morning dose of their

medication, but before the start of the 24-hour ECG recording. MRI or CT of the

brain was performed on all the patients.

A presurgical evaluation (MRI, 24-hour EEG –video telemetry recording)

was made for each subject before the decision about the implantation of VNS was

made, and none of the patients were considered eligible candidates for resective

epileptic surgery after these evaluations (Study IV).

4.2.2 Adjustment and use of vagus nerve stimulator (Study IV)

VNS (the model 100 Neurocybernetic prosthesis; Cyberonics, Pulse Generator)

was implanted using a previously described method (Reid 1990) between

February 1999 and January 2002. The starting level of stimulation was 0.25mA,

stimulation frequency 30Hz, pulse width 500ms, on time 30sec and off time

5min. The output current was generally increased by 0.25mA every two weeks.

The output current was increased to a clinical response. If the patient experienced

intolerable side effects, the output current was decreased or the increase was

delayed for another two weeks. The mean (range) stimulation output intensity one

year after the VNS implantation was 2.9 (1.75–3.5) mA, stimulation frequency

30Hz, pulse width 500ms, on time 30sec and mean (range) off time 4.7 (3.0–5.0)

min.

57

4.2.3 Analysis of heart rate behaviour (Studies I-IV)

ECG recordings

All the subjects in all studies (I-IV) were monitored for 24 hours with an

ambulatory two-channel ECG recorder (CardioCorder® model 456A, Del Mar

Medical, Irvine, California, USA). In study I, the ECG recording was done once.

In studies II-III, the ECG recording was done at baseline and after a mean follow-

up of 6.1 years, and in study IV the ECG recording was done in all the patients

before and one year after the VNS implantation. In the control subjects the 24-

hour ECG recording was performed once. The patients with epilepsy and the

control subjects were allowed to perform their daily activities during the

recording. The patients with epilepsy were also asked to keep a diary to document

any seizures or all the activities during the recording.

The ECG data from the recordings were sampled digitally and transferred

from an Oxford Medilog scanner (Oxford Instruments, UK) to a microcomputer

for analysis of HR variability. All the RR interval time series were first edited

automatically, after which careful manual editing was performed by visual

inspection of the RR intervals. Each RR interval time series was passed through a

filter that eliminates premature beats and artefacts and deletes the filling gaps

(Huikuri et al. 1993, Makikallio et al. 1996). In the final analysis of linear

components of HR variability, 24-hour measurements were divided into segments

of 3,600 RR intervals, and in the analysis of non-linear components of HR

variability, 24-hour measurements were divided into segments of 3,600 and only

segments with more than 85% sinus beats were included. The mean values of the

night hours (from midnight to 6 AM) and the day hours (from 9 AM to 9 PM)

were calculated (Studies I, III, IV).

Time domain and spectral analysis

SDNN from the entire recording was used as a time domain measure of HR

variability. In the frequency domain analysis of HR variability, a linear detrend

was applied to the RR interval data segments of 512 samples to make them more

stationary. The size of 20 was used as the model order in the analysis of the RR

interval data. The power spectra were quantified by measuring the area in three

frequency bands: 0.005 to 0.04 Hz, VLF, 0.04 to 0.15 Hz, LF and 0.15 to 0.4 Hz,

HF. The VLF power spectra were analysed and calculated from the entire

58

recording period, while the LF and HF power spectra were analysed from the

time window of 512 RR intervals, as recommended by the Task Force of the

European Society of Cardiology and the North American society of Pacing and

Electrophysiology (Task Force 1996).

Poincaré plot analysis

For quantitative two-dimensional vector analysis, the standard deviation of

instantaneous beat-to-beat RR interval variability (SD1) and continuous long-

term RR interval variability (SD2) were analysed, and visually presented as

Poincaré plot scattergrams, in which each RR interval is plotted as a function of

the previous one (Tulppo et al. 1996). In the computerized analysis, the Poincaré

plot was first turned 45° clockwise, and the standard deviation of the plot data

was then computed around the horizontal axis, passing through the data centre

(SD1). The standard deviation of the continuous long-term RR intervals was

quantified by turning the plot 45° counterclockwise (SD2) and by computing the

data points around the horizontal axis, passing through the centre of the data.

(Myllylä et al. 2002)

Approximate entropy analysis

A value of ApEn is a measure that quantifies the regularity of time series data. It

measures the logarithmic likelihood that runs of patterns (beat-to-beat difference

of RR interval length) are close in the next incremental comparisons. A greater

likelihood of remaining close (high regularity) produces smaller ApEn values, and

conversely, random data produce higher values. Two input variables, m and r,

must be fixed to compute ApEn, and m=2 and r=20% of the standard deviation of

the data sets were chosen as suitable values on the basis of previous findings of

good statistical validity. (Pincus & Viscarello 1992, Pincus & Goldberger 1994,

Myllylä et al. 2000)

Fractal correlation analysis

To quantify fractal correlation properties of HR, the detrended fluctuation

analysis technique, which is modified root-mean-square analysis of random walk,

was used. The HR correlation properties were defined for the short-term (below

59

11 beats, α) correlation of RR interval data (short-term scaling exponent).

(Mäkikallio et al. 1997, Myllylä et al. 2002)

Power-law relationship analysis

The power-law relationship of RR interval variability, a spectral measure

reflecting the distribution of the spectral characteristics of the RR interval

oscillations, was calculated from the frequency range of 10-4 to 10-2. The point

power spectrum was logarithmically smoothed in frequency domain, and the

power was integrated into bins spaced 0.0167 log(Hz) apart. A robust line fitting

algorithm of log(power) on log(frequency) was then applied to the power

spectrum between 10-4 and 10-2, and the slope of this line was calculated. This

frequency band was chosen on the basis of previous observations regarding the

linear relationship between log(power) and log(frequency) in this frequency band.

(Saul et al. 1988, Bigger, Jr. et al. 1996, Myllylä et al. 2002)

4.2.4 Statistical analysis

All data were analysed using the SPSS for Windows SPSS versions 10.0 (I), 11.5

(IV) and 14.0 (II-III) (SPSS Inc. Chicago, Illinois, USA). The results are mostly

given as medians (interquartile range). The changes of continuous variables

during the follow-up were analysed by the Mann-Whitney two-sample test. Two-

sided p-values were used. The Mann-Whitney two-sample test was used due to a

small number of cases and because of a normal distribution pattern could not be

detected. When comparisons were made between the groups, the Wilcoxon

signed-rank test was used. Spearman’s correlation coefficients were used to

estimate the correlation of the HR variables with the duration of epilepsy and the

age of the patients. The Mann-Whitney two-sample test was also used to analyse

the association of HR variability with the laterality of the seizure focus and the

carbamazepine (CBZ) and oxcarbazepine (OXC) monotherapy. The Mann-

Whitney two-sample test was also used to analyse the association of HR

variability with the laterality of the seizure focus and the carbamazepine (CBZ)

and oxcarbazepine (OXC) monotherapy (Studies I-III). The limit of statistical

significance was set at p<0.05 in all studies.

60

61

5 Results

5.1 Clinical evaluation of autonomic nervous system function

The patients did not complain of any particular symptoms referring to ANS

dysfunction. In the clinical examination, no signs of autonomic dysfunction were

found. The clinical cardiorespiratory findings, including baseline blood pressure

and neurological examination, were normal in all patients. None of the patients

had significant cardiac arrhythmias in the ECG recordings. The results of the

basic laboratory screening tests were within normal range and serum drug levels

within therapeutic ranges in all the patients.

In studies II-III, during the follow-up, one patient with refractory TLE

underwent resective surgery (mesial temporal lobectomy) and became seizure

free. One patient with well-controlled TLE at baseline experienced an increase in

seizure frequency during the last year of the follow-up. Four patients with well-

controlled TLE had remained seizure-free and were on no medication at the

follow-up.

5.2 Cardiac regulation in temporal lobe epilepsy

5.2.1 Long-term heart rate dynamics (Study II)

In study II, all mean values of HR variability measures tended to be lower in

patients with refractory TLE compared to those with well-controlled TLE at

baseline and after the follow-up, although statistically significant differences

could not be found (p>0.05) (Table 5). After the follow-up, the Poincaré

components SD1 (p=0.039) and SD2 (p=0.001) were further decreased in patients

with refractory TLE compared to the baseline values. There were no statistically

significant differences in any HR variability measures (p>0.05) in patients with

well-controlled TLE when the follow-up values were compared to the baseline.

Figure 2 presents representative examples of the power spectrum analysis of HR

variability.

62

Table 5. Measures of HR and HR variability in patients with refractory and well-

controlled temporal lobe epilepsy at baseline and after follow-up of mean duration of 6

years (Study II).

Variables Refractory Well-controlled

Baseline At 6 years Baseline At 6 years

(n=18) (n=18) (n=18) (n=18)

RRI (ms) 823 (775–920) 782 (734–830)* 823 (789–897) 778 (720–861)

SDNN(ms) 143 (126–170) 129 (105–147) 155 (107–189) 175 (121–198)

LF (ms x ms) 776 (523–979) 612 (392–921) 998 (664–1377) 893 (501–1710)

HF (ms x ms) 374 (265–769) 263 (210–564) 550 (383–931) 510 (222–865)

VLF (ms x ms) 1321 (1017–1781) 1119 (853–1738) 1630 (1096–2520) 1706 (1049–2849)

SD1 24 (19–34) 22 (17–27)* 28 (20–37) 28 (20–34)

SD2 118 (101–138) 96 (86–107)** 125 (99–161) 115 (96–147)

α 1.20 (1.09–1.36) 1.21 (1.11–1.30) 1.23 (1.11–1.33) 1.21 (1.16–1.27)

Slope β −1.3 (−1.5…−1.2) −1.29 (−1.34…−1.17) −1.3 (−1.5…−1.2) −1.28(−1.35…−1.16)

RRI, R-R interval; SDNN, SD of all RR intervals; LF, low-frequency; HF, high-frequency; VLF, very low-

frequency; SD1, beat-to-beat variability measure from Poincaré; SD2, long-term variability measure from

Poincaré; α, short-term fractal correlation parameter; Slope β, long-term power-law slope. Values are

presented as medians (interquartile range).

*p<0.05, **p<0.01 compared both groups separately before and after follow-up, the Wilcoxon signed-rank

test.

Fig. 2. Example of two-dimensional vector analysis (Poincaré plots) in a healthy

subject (A), in a patient with a well-controlled TLE (B) and in a patient with refractory

TLE (C)

5.2.2 Circadian heart rate variation (Study I)

The HR variability measurements showed that patients with TLE have

dysfunction of autonomic cardiac regulation which is more pronounced during

the night time. The SDNN (p=0.001), the spectral components LF (p=0.001) and

CRefractory patient

A

Healthy subject

B

Well-controlledpatient

63

HF (p=0.004), the SD1 (p=0.001) and SD2 (p=0.001) Poincaré components and

the short-term fractal property component α (p=0.01) of the TLE patients were

significantly lower than those of the control subjects during the night and also

during the day (p<0.05). The suppression of the circadian fluctuation of the HF

and LF spectral components in TLE patients compared to the healthy control

subjects is presented in Figure 3. There were no differences in any HR variability

measurements (p>0.05), apart from ApEn in the night (p=0.026), between the

refractory and well-controlled epilepsy patients when compared to each other.

The mean night-to-day ratios (median, interquartile range) of the SDNN, SD1 and

SD2 Poincaré components were lower in the epilepsy patients (SDNN 0.80, 0.68–

1.07, p=0.014; SD1 1.16, 0.98–1.44, p=0.030; SD2 0.78, 0.62–0.92, p=0.007) than

in the control subjects (SDNN 1.07, 0.90–1.17, SD1 1.48, 1.07–1.70, SD2 0.98,

0.74–0.97). There were no differences in the mean night-to-day ratios in the

spectral components LF and HF, ApEn and the short-term fractal property

component α (p>0.05) between patients with TLE and the control subjects.

Furthermore, none of the night-to-day ratios of the HR variability measures were

different between the refractory and the well-controlled epilepsy patients

(p>0.05).

64

Fig. 3. The 24-hour circadian fluctuation of low-frequency (LF) and high-frequency (HF)

components of HR variability (medians) in patients with TLE (circles) and in healthy

controls (squares). *p<0.05, **p<0.01, ***p<0.001 for comparison between patients and

controls, Mann-Whitney two-sample test.

***

***

***

***

***

*** *

**

*

**

**

***

*

**

**

**

**

*

*

**

**

**

**

** *

**

0

500

1000

1500

2000

2500

3000

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7

Time (hour of the day)

LF

po

wer

(ms

xm

s) ***

***

**

**

**

0

200

400

600

800

1000

1200

1400

1600

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7

Time (hour of the day)

HF

po

wer

(ms

xm

s) *

65

5.2.3 Long-term changes in circadian heart rate variation (Study III)

In study III the circadian HR variability was reduced in patients with TLE at

baseline when compared to healthy control subjects. After 6 years’ follow-up, the

spectral components LF (p=0.039) and HF (p=0.001), and the Poincaré

component SD1 (p=0.002) were further decreased in patients with refractory TLE

compared to the baseline values during the night. During the day, the spectral

components LF (p=0.004) and HF (p=0.006) and the Poincaré components SD1

(p=0.006) and SD2 (p=0.001) were further decreased in patients with refractory

TLE compared to the baseline values (Tables 6 and 7). There were no statistically

significant differences in any HR variability measures (p>0.05) in patients with

well-controlled TLE when the baseline values were compared to those at the

follow-up.

Ta

ble

6. T

he n

igh

t ti

me

(0

0.0

0 A

M-0

6.0

0 A

M)

va

lue

s f

or

the

tim

e d

om

ain

, fr

eq

ue

ncy

do

ma

in a

nd

fra

cta

l m

eas

ure

s o

f H

R v

ari

ab

ilit

y

in p

ati

en

ts w

ith

re

fra

cto

ry a

nd

we

ll-c

on

tro

lled

ep

ile

psy

an

d in

co

ntr

ol

su

bje

cts

(S

tud

y I

II).

Variable

s P

atie

nts

with

epile

psy

C

ontr

ols

Refr

act

ory

W

ell-

contr

olle

d

Base

line

At 6 y

ears

B

ase

line

At 6 y

ears

B

ase

line

(n=

18)

(n=

18)

(n=

18)

(n=

18)

(n=

36)

LF

(m

s x

ms)

613 (

452–1170)+

+

546 (

347–1017)*

884 (

584–1244)+

+

835 (

542–2022)

2386 (

1010–3252)

HF

(m

s x

ms)

493 (

310–1158)+

317 (

199–588)**

631 (

289–838)+

717 (

338–1054)

1156 (

412–2938)

SD

1

27 (

21–39)+

+

22 (

18–32)*

31 (

20–34)+

+

27 (

18–41)

39 (

31–69)

SD

2

89 (

80–121)+

+

81 (

68–102)

105 (

82–117)+

+

115 (

73–151)

147 (

106–185)

α

1.0

7 (

0.8

6–1.2

4)

1.1

4 (

0.9

8–1.2

6)

0.9

6 (

0.7

8–1.1

8)

1.0

8 (

0.9

7–1.2

1)

1.0

3 (

0.8

2–1.2

1)

Ap

En

1.3

9 (

1.3

0–

1.4

9)

1.3

0 (

1.2

0–

1.4

6)

1.2

6 (

1.1

1–

1.4

5)

1.2

7 (

1.1

2–

1.3

6)

1.2

8 (

1.1

1–

1.4

6)

LF

, lo

w-f

req

ue

ncy

; H

F,

hig

h-f

req

ue

ncy

; S

D1,

be

at-

to-b

ea

t va

ria

bili

ty m

ea

sure

fro

m P

oin

caré

; S

D2

, lo

ng-t

erm

vari

abili

ty m

easu

re f

rom

Poin

caré

; α

, sh

ort

-te

rm

fra

cta

l co

rre

latio

n p

ara

me

ter;

Ap

En

, a

pp

roxi

ma

te e

ntr

op

y. V

alu

es

are

pre

sente

d a

s m

edia

ns

(inte

rquart

ile r

ange).

* p

<0.0

5,

** p

<0.0

1 f

or

com

pariso

n p

atie

nts

with

refr

act

ory

and w

ell-

contr

olle

d T

LE

separa

tely

befo

re a

nd a

fter

follo

w-u

p, th

e W

ilcoxo

n s

ign

ed

-ra

nk

test

. +p

<0

.05

, +

+p

<0.0

1 f

or

com

pariso

n b

etw

een p

atie

nts

with

refr

act

ory

and w

ell-

contr

olle

d T

LE

separa

tely

and c

ontr

ols

, M

ann-W

hitn

ey

two-s

am

ple

test

.

66

Ta

ble

7. T

he d

ay

-tim

e (

09

.00 A

M –

09

.00

PM

) va

lue

s f

or

the t

ime d

om

ain

, fr

eq

uen

cy d

om

ain

an

d f

racta

l m

easu

res o

f H

R v

ari

ab

ilit

y

in p

ati

en

ts w

ith

re

fra

cto

ry a

nd

we

ll-c

on

tro

lled

ep

ile

psy

an

d in

co

ntr

ol

su

bje

cts

(S

tud

y I

II).

Variable

s P

atie

nts

with

epile

psy

C

ontr

ols

Refr

act

ory

W

ell-

contr

olle

d

Base

line

At 6 y

ears

B

ase

line

At 6 y

ears

B

ase

line

(n=

18)

(n=

18)

(n=

18)

(n=

18)

(n=

36)

LF

(m

s x

ms)

733 (

545–1152)+

+

581 (

346–697)**

1043 (

653–1318)+

894 (

453–1266)

1520 (

865–2227)

HF

(m

s x

ms)

2

71

(1

87

–5

06

) 194 (

137–325) *

* 353 (

228–632)

251 (

141–603)

467 (

218–1329)

SD

1

21 (

19–29)+

19 (

15–23) *

* 24 (

22–32)

22 (

18–29)

29 (

20–47)

SD

2

112 (

102–133)+

+

98 (

90–107) *

* 120 (

104–140)+

104 (

90–143)

141 (

124–176)

α

1.2

6 (

1.1

7–1.3

1)

1.2

1 (

1.1

4–1.3

5)

1.2

5 (

1.1

9–1.3

7)

1.2

8 (

1.1

7–1.3

4)

1.2

7 (

1.0

8–1.3

9)

Ap

En

1.1

0 (

1.0

3–

1.2

0)

1.1

4 (

1.0

3–

1.2

9)

1.1

6 (

1.0

8–

1.2

6)

1.1

6 (

1.0

7–

1.3

1)

1.1

2 (

0.9

7–

1.2

4)

LF

, lo

w-f

requency

; H

F, hig

h-f

requency

; S

D1, beat-

to-b

eat va

riabili

ty m

ea

sure

fro

m P

oin

caré

; S

D2

, lo

ng

-te

rm v

aria

bili

ty m

ea

sure

fro

m P

oin

caré

; α

, sh

ort

-te

rm

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68

Six patients with refractory TLE reported having experienced a partial seizure

during the 24-hour ECG recording at the baseline, and three patients with

refractory TLE reported having experienced a partial seizure during the second

24-hour ECG recording in Studies II-III. Analysis of HR variability of these

patients showed no differences compared with the analysis of HR variability in

the patients without seizures.

Altered HR variability was not associated with the AEDs used, with the

duration of TLE or the age of the patients. Furthermore, there was no correlation

between altered cardiac autonomic control and the lateralization of the seizure

focus (p >0.05) (Studies I-III).

5.3 Effect of vagus nerve stimulation on heart rate dynamics

(Study IV)

Seizure reduction of ≥50% (responder) was observed in nine patients (64%). The

non-responder group consisted of three patients who had experienced <50%

seizure reduction, and two patients who did not experience any change in seizure

frequency during VNS treatment.

The median value of the RR interval (p=0.008), SDNN (p<0.001), the

spectral components VLF (p<0.001), LF (p=0.001) and HF (p=0.002), and the

Poincaré components SD1 (p=0.005) and SD2 (p<0.001) of the patients with

refractory epilepsy were significantly lower than those of the control subjects

before VNS implantation. A similar decrease in HR variability measures was

found one year after VNS implantation (p<0.05) (Table 8).

There were no differences in any HR variability measurements in patients

with refractory epilepsy before and one year after the implantation of the VNS.

The median value of the RR interval (p=0.020), SDNN (p=0.045), VLF

(p=0.014), LF (p=0.014), HF (p=0.014), SD1 (p=0.009), SD2 (p=0.028) and the

power-law slope β (p=0.042) of the non-responders were significantly lower than

those of the responders one year after VNS implantation.

In study IV the high interindividual variability of documented seizures during

the 24-hour ECG recordings did not allow a meaningful statistical analysis of the

effects of seizures on HR variability. No significant arrhythmias were seen during

ECG recordings (study I-IV).

69

Table 8. Measures of HR variability in control subjects and in patients with refractory

epilepsy before and 1 year after the implantation of vagus nerve stimulator (Study IV).

Variables Control subjects Patients with epilepsy

Before VNS implantation One year after VNS

implantation

(n=28) (n=14) (n=14)

RRI (ms) 854 (809–922) 785 (722–826)** 750 (670–830)**

SDNN (ms) 181 (150–214) 127 (100–157)*** 116 (107–147)***

VLF (ms x ms) 3296 (1963–5604) 853 (609–1214)*** 984 (509–1918)***

LF (ms x ms) 1740 (1123–3275) 456 (327–1051)** 474 (179–694)***

HF (ms x ms) 1054 (315–2291) 232 (189–610)** 215 (166–396)***

SD1 34 (25–60) 21 (17–27)** 20 (17–26)***

SD2 143 (125–190) 94 (81–117)*** 90 (74–124)***

α 1.24 (1.06–1.30) 1.15 (1.08–1.17) 1.18 (1.09–1.23)

Slope β −1.14 (−1.37…−1.07) −1.28 (−1.40…−1.24) −1.33 (−1.45…−1.23)

RRI, RR interval; SDNN, SD of all NN intervals; VLF, very low-frequency; LF, low-frequency; HF, high-

frequency; SD1, beat-to-beat variability measure from Poincaré; SD2, long-term variability measure from

Poincaré; α, short-term fractal correlation parameter; slope β, long-term power-law slope. Values are

presented as medians (interquartile range).

**p<0.01, ***p<0.001 compared to control subjects, the Mann-Whitney U-test

70

71

6 Discussion

6.1 General aspects

Epilepsy is known to be associated with autonomic dysfunction during epileptic

seizures (Nei et al. 2000, Opherk et al. 2002), but there is also increasing

evidence of interictal ANS dysfunction in patients with chronic epilepsy

(Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Ansakorpi et al.

2000, Ansakorpi et al. 2002, Mukherjee et al. 2009). Previous studies have

analysed changes in HR variation by using short-term ECG recordings or by

analysing ECG recordings during 24-hour time period without analysing day and

night time separately, even though it is well documented that there are circadian

rhythms seen in many physiological events such as HR, thermoregulation,

wakefulness and sleep (Hastings et al. 2007). Furthermore, it is well documented

that there are characteristic diurnal patterns for cardiovascular events e.g. acute

myocardial infarction (Muller et al. 1985) and sudden cardiac death (Willich et al.

1987), as well as for epileptic seizures (Quigg et al. 1998, Hofstra et al. 2011).

Decreased circadian HR fluctuation has been reported in several

cardiovascular and neurological diseases (Bernardi et al. 1992, Chakko et al.

1993, Huikuri et al. 1994, Korpelainen et al. 1997, Pursiainen et al. 2002),

although the clinical significance of these findings has remained undefined. There

are no previous studies regarding changes in circadian HR variation in patients

with epilepsy. Moreover, there is no information about changes in long-term

cardiovascular autonomic regulation in patients with TLE assessed by HR

variability measurements in a longitudinal setting.

The present study was designed to evaluate long-term changes in HR

behaviour in patients with TLE. Special attention was also paid to analysing

changes in circadian HR variation in patients with well-controlled or refractory

TLE by measuring HR variability from 24-hour ambulatory ECG recordings

separately during the night and day time. With prospective follow-up studies the

focus of the research was also put on evaluating possible progressive changes in

cardiac regulation over time in patients with chronic TLE. Finally, the effects of

VNS treatment on cardiac regulation in patients with epilepsy were evaluated in a

prospective setting by analysing HR variation from 24-hour ECG recordings in

patients with chronic epilepsy prior to and one year after onset of VNS treatment.

The ultimate goal of this type of research is to identify HR variation

indicators that could in the future be used to predict the risk of SUDEP or other

72

untoward cardiac events in individual epilepsy patients. In this study, for the first

time, altered circadian cardiac autonomic control was observed in patients with

TLE, showing that the reduced HR variation is more pronounced during night

than during day time in these patients.

6.2 Clinical findings of autonomic nervous system function in

patients with epilepsy

It is well established that both partial and generalized epileptic seizures may be

accompanied by altered autonomic function characterized by multiple types of

symptoms and signs, such as cardiac arrhythmias, changes in blood pressure,

sweating, bowel and bladder dysfunction and incontinence, etc. These types of

symptoms and signs can occur between (interictally), during (ictally) and after

(postictally) epileptic seizures. In this study careful clinical evaluation of ANS

function (clinical examination and detailed, structured medical history using a

questionnaire) did not reveal any symptoms or signs of autonomic dysfunction,

even though altered regulation of cardiac function was detected by analysing HR

variation during a 24-hour period, and later in the analysis of circadian HR

behaviour in particular in the same patients, as described below (Studies I-IV).

6.3 Cardiac regulation in temporal lobe epilepsy

6.3.1 Long-term heart rate dynamics

This first long-term follow-up study on the effects of chronic TLE on HR

variability showed that the reduction in HR variability is progressive in chronic

refractory TLE whereas in patients who remained well-controlled with the

treatment no further decrease in HR variability was seen during the follow-up

(Study II). The findings of the present study are in accordance with results from

the previous studies showing reduced HR variability in patients with chronic TLE

(Massetani et al. 1997, Ansakorpi et al. 2002, Hilz et al. 2002).

Significant reductions of HR variability were observed in quantitative

measures of Poincaré plots. SD2 mainly describes long-term HR variability and

SD1 short-term variability. The former reflects partly physical activity of the

patients in addition to autonomic regulation of HR (Tulppo & Huikuri 2004). The

latter index is a more direct measure of cardiac vagal outflow. In fact, SD1 is a

more reliable index of cardiac vagal activity than the high-frequency spectral

73

component of HR variability, when measured from ambulatory recordings

(Tulppo et al. 1998). The present results suggest that refractory TLE is mainly

related to progressive diminution of cardiac vagal outflow. It is also possible that

physical daily activity is reduced in refractory TLE during the time course, partly

explaining the marked reduction in SD2 index of Poincaré plots.

In TLE seizures arise from mesial temporal structures, and damage in those

areas may result in abnormalities manifesting as attenuation of HR variation. It

has been proposed that decrease in HR variability is partly due to functional

rather than structural changes in TLE (Ansakorpi et al. 2004). One study (Persson

et al. 2006) where HR variation was studied in patients with epilepsy surgery

suggested that there may be a pre-existing biologic difference between patients

who become seizure-free after surgery and those who do not. Furthermore, it

seems that the anterior part of the temporal lobe does not play a major role in the

circadian regulation of HR variability. On the contrary, one recent study

suggested that hippocampal sclerosis may be associated with autonomic

dysfunction in patients with TLE (Koseoglu et al. 2009). The design of the

present study did not allow drawing any conclusions about the possible

association between the damage to specific temporal lobe structures and the

observed changes in autonomic cardiovascular control.

HR variability has been shown to decrease with normal ageing in healthy

people. It is typically lower in elderly people compared to middle-aged or young

subjects, and these age-related changes seem to be modified by gender (Hayano et

al. 1991, Pikkujämsä et al. 1999, Fukusaki et al. 2000, Jokinen et al. 2005).

Therefore, ageing may also have had an effect on the present longitudinal results.

However, after the six-year follow-up the observed changes in the HR variation

were more evident in patients with refractory TLE than in patients with well-

controlled TLE after the follow-up, which suggests that other reasons than ageing

were more important in contributing to the observed changes in the HR variation

in the present study.

6.3.2 Circadian heart rate variation

The literature concerning circadian HR variation in epilepsy is scarce. There is

only one study evaluating cardiac autonomic function during night time in

children with partial epilepsy (Ferri et al. 2002). In that study, it was

demonstrated that during sleep, patients with epilepsy tended to have overall

lower HR variability in both time- and frequency domain parameters. Similar

74

dysfunction in autonomic cardiac regulation during the night time was observed

in patients with TLE in the present study. However, day time values were also

decreased in the present patients when the values were compared to healthy

control subjects (Study I). The lower values in HR variability measurements were

seen especially in the LF and HF power spectral components and SD1 and SD2

measures of Poincaré analysis when the values of patients with TLE were

compared to those of control subjects. In accordance with the present studies,

Persson et al. reported a similar non-specific disturbed autonomic cardiac

function during the night time that was not present in newly diagnosed

localization-related epilepsy until CBZ treatment was started (Persson et al.

2007).

The present finding of suppressed circadian HR variation, especially during

the night, is of particular interest in relation to SUDEP. Many SUDEP cases seem

to share some similar features related to the circumstances of death. Patients are

often found in bed or are known to have been asleep before death (Leestma et al.

1989, Nashef et al. 1996, Kloster & Engelskjøn 1999, Nei et al. 2004, Nobili et

al. 2010) suggesting that the risk for specific aetiologic mechanisms directly

responsible for death may increase during sleep, and sleep-related seizures could

differ pathophysiologically (Tomson et al. 2008). Of interest, the present results

showed that suppression of HR variability in TLE is most pronounced at night,

suggesting a night time parasympathetic dysfunction for patients with epilepsy.

Furthermore, these results suggest that reduced HR variability may be one

contributing mechanism by which patients with refractory TLE are subject to an

increased risk of SUDEP. In the current study, both patients with refractory and

well-controlled TLE had diminished circadian HR variation, while the observed

changes were more evident in patients with refractory TLE. The present findings

support the view that patients with recurrent seizures are at greater risk for

dysfunction of autonomic cardiac regulation (Ansakorpi et al. 2000, Ansakorpi et

al. 2002, Persson et al. 2005, Mukherjee et al. 2009). However, the present

observation that suppressed circadian HR variation was also present in patients

with well-controlled TLE indicates that cardiac regulation is also altered in

patients whose epilepsy seems to be well managed and who do not present with

seizures while being treated. Consistent with this it is well known that SUDEP

may also occur in patients with well-controlled seizures (Harvey et al. 1993,

Racoosin et al. 2001, Langan et al. 2005).

To our knowledge, for the first-time, the present study showed that patients

with TLE have altered circadian HR variation with more pronounced decrease in

75

HR variation during the night than during the day. Furthermore, the nocturnal

increase in HR variation usually seen in the normal population could not be

detected in patients with refractory epilepsy (Studies I,IV).

6.3.3 Long-term changes in circadian heart rate variation

In this long term follow-up study, the present results showed that patients with

refractory TLE with recurrent seizures had progressive diminution of the HR

variation both in day and night time (Study III). Especially the LF and HF power

spectrum components of HR variability and the SD1 and SD2 of the Poincaré

analysis were further decreased, implicating extensive disruption of the

autonomic control of the heart. The present results are in accordance with

previous studies showing that chronic epilepsy affects the autonomic cardiac

regulation system and that both the sympathetic and parasympathetic system are

affected (Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Tomson

et al. 1998, Ansakorpi et al. 2000, Harnod et al. 2008, Ansakorpi et al. 2011)

Patients with refractory epilepsy are thought be at greater risk for SUDEP

(Tomson et al. 2008). More attention has also been paid to possible association of

altered cardiovascular autonomic regulation with the pathogenesis of SUDEP

(Nashef et al. 1996, Nei et al. 2000). One recent study suggested that although

reduced HR variation is not considered a direct indication of elevated risk of

SUDEP, the inferior capacity of the ANS to react during dynamic situations may

be an indicator of potential autonomic imbalance during generalized epileptiform

seizures (Ansakorpi et al. 2011). Of interest, the present study showed that the

HR variation is progressive in patients with refractory TLE with recurrent

seizures, although the circadian HR dynamics do not seem to change further with

longer duration of chronic TLE. These results suggest that in patients with

epilepsy reduced circadian HR variation may reflect refractoriness of the

condition, and increased risk of SUDEP. Furthermore, it seems that suppression

of circadian HR variability, especially during the night, may occur early on in

epilepsy. Analysis of circadian HR variation from 24-hour ECG recordings adds

value to the evaluation of HR variability in patients with epilepsy, and may

eventually help in identifying patients at greater risk for dysfunction of autonomic

regulation of HR with possible undesirable outcomes, especially SUDEP.

76

6.3.4 Effect of antiepileptic medication on heart rate variation

The possible role of AEDs in altering HR variation is interesting, but the effect of

different AEDs on HR variability is difficult to distinguish from that of the

epilepsy itself. AEDs have direct effects on the cardiac conduction system, e.g. by

blocking sodium channels, but they may also act indirectly through a central

effect mediated by autonomic nervous control of the heart. Previous studies

suggest that CBZ therapy may be associated with decreased HR variability

(Devinsky et al. 1994, Isojärvi et al. 1998, Tomson et al. 1998, Ansakorpi et al.

2000, Persson et al. 2003), and one study in patients with medically intractable

epilepsy reported increased cardiac sympathetic activity during sleep induced by

sudden discontinuation of CBZ (Hennessy et al. 2001). Furthermore, CBZ

treatment has also been suggested to be associated with an increased risk of

SUDEP, but the reports are controversial (Kennebäck et al. 1997, Timmings 1998,

Nilsson et al. 1999, Walczak et al. 2001, Hesdorffer et al. 2011). In this regard,

LTG has also been suggested to be associated with an increased risk of SUDEP in

patients with idiopathic epilepsy (Aurlien et al. 2007). This finding is supported

by a pooled analysis of previous case control studies, in which LTG therapy was

associated with significantly increased risk for SUDEP (Hesdorffer et al. 2011).

In the present study, no correlation was found between altered HR variability

and any particular AEDs used. Nor were any clinically significant cardiac

arrhythmias observed in the study population. However, all present patients with

refractory TLE and the majority of patients with well-controlled TLE were taking

a voltage-dependent sodium channel blocker with or without other AEDs. Patients

with refractory TLE were also more often on polytherapy while well-controlled

patients were on monotherapy. (Studies I-III) Therefore, it is not possible to

assess individual effects of different types of AEDs on HR variation in this

population. Small sample size also reduces the likelihood of being able to show

statistically significant differences between the treatment groups in relation to

HR. Therefore, the possible effect of AEDs on HR variation needs to be further

evaluated in larger patient populations in the future.

6.4 Effect of vagus nerve stimulation on heart rate dynamics

The results of previous studies on possible effect of VNS treatment on cardiac

autonomic function are controversial. One study suggested that VNS treatment

may have some effect on cardiac autonomic function with a reduction of the HF

77

component during the night and a flattening of sympathovagal circadian changes

(Galli et al. 2003). On the other hand, other short-term studies have suggested

that VNS does not have an effect on the HR (Handforth et al. 1998, Setty et al.

1998) or cardiac rhythm during sleep (Murray et al. 2001).

The present results suggest that one-year treatment with VNS does not have a

marked effect on HR variability (Study IV). Interestingly, one year after the

implantation of VNS, most of the patients had experienced seizure reduction by

more than 50%, but this reduction was not associated with changes in HR

variability in the patient group as a whole. It was also found that there were no

differences in HR variation between subjects who turned out to be responders and

subjects who turned out to be non-responders to the VNS treatment in the analysis

of the ECG recordings that were obtained prior to the VNS implantation. The

present results suggest that long-term VNS treatment does not affect autonomic

nervous system function as reflected by HR variability. These result are also

consistent with findings suggesting that epilepsy may itself alter HR variation in

the long term independently of seizure recurrence (Ansakorpi et al. 2004)

VNS treatment has been shown to be safe, well tolerated and effective in

seizure reduction (Amar et al. 1999, Schachter 2004, Wheeler et al. 2011).

Consistent with these findings, in the present study VNS treatment was well

tolerated without major adverse effects and most of the patients experienced more

than 50% seizure reduction during the first year of treatment.

6.5 Methodological considerations

The individual studies were designed carefully, but there are certain limitations in

the design that may have influenced the results and need to be considered when

interpreting the current findings.

The number of participants in each individual study was small. Due to the

small number of subjects, the applicable statistical analysis method options

available were limited. It is also possible that some differences in various

comparisons that were not statistically significant in the analyses might have

reached statistical significance if a larger number of subjects had been available

for these comparisons. In addition, there was no control group in Study III for

prospective evaluation, and information about possible prospective changes in

HR variation in a non-epileptic subjects was therefore not available for

comparison.

78

Physical activity is known to increase HR variability. However, the design of

the present study did not allow analysis of the effect of physical activity on HR

variability in the study subjects.

In summary, larger studies are needed to confirm the present findings and to

establish the utility of these HR variability measurements for routine use.

79

7 Conclusions

1. HR variation is reduced in patients with chronic TLE, indicating an altered

cardiovascular regulation in these subjects. The reduction in HR variation is

more pronounced in subjects with refractory TLE than in subjects with well-

controlled TLE.

2. HR variation further decreases with time in refractory TLE, but not in well-

controlled TLE.

3. The circadian HR dynamics alter in TLE, i.e. the decrease in HR variation is

more evident during the night than during the day. This alteration in circadian

HR dynamics is evident both in patients with refractory TLE and in subjects

with well-controlled TLE, but it is more pronounced in subjects with

refractory TLE.

4. Even though the HR variation decreases with longer duration of refractory

TLE, the altered circadian HR dynamics do not seem to change further with

longer duration of chronic TLE.

5. Despite the important role of the vagal nerve in the regulation of HR, it seems

that VNS treatment for chronic epilepsy does not have a significant impact on

cardiovascular function evaluated by HR variation in 24-hour ECG

recordings.

80

81

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Original publications

I Ronkainen E, Ansakorpi H, Huikuri HV, Myllylä VV, Isojärvi JIT & Korpelainen JT (2005) Suppressed circadian heart rate dynamics in temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 76(10): 1382–1386.

II Suorsa E, Korpelainen JT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Isojärvi JIT (2011) Heart rate dynamics in temporal lobe epilepsy – a long term follow-up study. Epilepsy Research 93(1): 80–83.

III Suorsa E, Isojärvi JIT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Korpelainen JT (2011) Long-term changes in circadian heart rate variability in patients with temporal lobe epilepsy. Manuscript

IV Ronkainen E, Korpelainen JT, Heikkinen E, Myllylä VV, Huikuri HV & Isojärvi JIT (2006) Cardiac autonomic control in patients with refractory epilepsy before and during vagus nerve stimulation treatment – a one year follow-up study. Epilepsia 47(3): 556–562.

Reprinted with permission from BMJ Group (I), Elsevier (II) and Blackwell

Publishing, Inc. (IV).

Original publications are not included in the electronic version of the dissertation.

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ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY

UNIVERSITY OF OULU,FACULTY OF MEDICINE,INSTITUTE OF CLINICAL MEDICINE,DEPARTMENT OF NEUROLOGY