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Automated Neonatal Seizure DetectionStephen Daniel Faul 1st August 2007

A Thesis Submitted to the National University of Ireland, Cork in Fulllment of the Requirements for the Degree of Doctor of Philosophy

Supervisors: Head of Department:

Dr. William Marnane & Dr. Gordon Lightbody Prof. Patrick Murphy

Department of Electrical and Electronic Engineering, National University of Ireland, Cork.

AbstractSeizures occur commonly in the Neonatal Intensive Care Unit (NICU). They are an important clinical consequence of central nervous system diseases in the newborn including brain haemorrhage, stroke, meningitis and hypoxic-ischaemic encephalopathy. As clinical signs can be absent during neonatal seizures, the electroencephalograph (EEG) is the primary tool for their detection to allow for the administration of treatment.

Compact digital video EEG recording systems are now available that are suitable for use in the NICU. However, particular skills are required to interpret the complex neonatal EEG and most neonatal units lack this expertise. While some NICUs rely on cerebral function monitoring devices (CFMs) to assess neurological function, these systems are unreliable in the hands of non-experts and artifacts can often be mistaken as seizures. Focal and short duration seizures also often go undetected.

Thus there is a great need for an accurate, automated neonatal seizure detection system for the NICU, which can provide around-the-clock monitoring of patients with little or no input from medical staff. The aim of this thesis is to develop such a system, and in particular to overcome the problems inherent to previous attempts at automated neonatal seizure detection. One of the main problems facing accurate neonatal seizure detection is the presence of artifacts in the EEG which can mimic seizure waveforms causing false alarms. Furthermore, there are many ways in which information can be extracted from the EEG which have not, up to now, been utilised. Finally, simple thresholding routines have often been used in making the nal decision on whether a seizure is occuring. These means of classication are unreliable in a complex problem such as neonatal seizure detection.

The work in this thesis details the application of novel mathematical and engineering methods to develop a system which addresses these problems. A novel means of artifact rejection is presented which, while reducing the amount of false alarms, allows for simultaneous multichannel analysis and the concentration of seizure activity. Following this step, information is extracted from the EEG using analysis methods from various areas of signal processing theory, from simple frequency analysis to nonlinear dynamics system theory and modelling algorithms. Finally, a number of classication methods are analysed and their performances compared to produce the most accurate system possible. The system is tested on a large data set of neonatal EEG and performs accurate

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seizure detection with a low false alarm rate. The proposed system is free from any requirement to retrain it on each patient. The system is compared to and outperforms previously proposed automated neonatal seizure detection methods.

One of major contributions made in this thesis to the area of neonatal seizure detection is the development of Gaussian modelling techniques for EEG analysis. These techniques outperform all of the other features tested in this work and provide a meaningful indicator of neonatal seizures. While this is a very important discovery, it would have led to no better system performance had artifact rejection techniques been explored. In this area this thesis proposes a robust technique for removing artifacts from EEG signals, while also reducing the amount of data which needs to be further analysed and enhancing any seizure activity present in the EEG. This development allows for the reduction of false alarms in the seizure detection system, a must when designing a system for clinical use.

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AcknowledgementsThere are many people who have helped me throughout the course of this thesis. First and foremost I would like to thank Dr. Liam Marnane who rst took on this project and allowed me to take part. Thanks to him the engineering group working on this problem has now grown to include Masters students, PhD students and postdoctoral researchers and biomedical signal processing lectures have been introduced to the undergraduate course. Without his knowledge, support and time this thesis would not have been possible.

I would also like to thank Dr. Geraldine Boylan who came to this department with the initial idea, and who provides all of the EEG recordings and clinical knowledge upon which this work is based. Thanks also go to Dr. Sean Connolly and the rest of the neonatal seizure research group for providing medical knowledge to back up the engineering research.

I would also like to thank Dr. Gordon Lightbody and Dr. Gregor Gregor i for their help, particucc larly in the area of Gaussian process modelling. All of the staff members in the Dept. of Electrical and Electronic Engineering in University College Cork have all assisted in some way or another, if not in work, then in friendship. I would also like to thank Ralph OFlaherty in particular, without whom efcient work in the department would be impossible.

Of course I must thank all of the postgraduate students in the department, those who have left before me and those who will remain after me, for their technical help and friendship, and for making the long hours spent working on this thesis almost seem like fun!

Finally, I would like to thank my family and Sin ad for their support, for keeping a roof over my e head and a smile on my face, and, of course, for listening to me talking about neonatal seizure detection for the last few years. Without your love and support this work would certainly not have been possible.

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Statement of OriginalityI hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of a university or other institute of higher learning, except where due acknowledgement is made in the text.

Stephen Faul

August 2007

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Contents

1 Introduction and Scope of this Thesis 1.1 1.2 1.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electroencephalogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 1.3.2 1.3.3 1.3.4 1.4 1.5 1.6 The Evolution of EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrode Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Montages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 2 2 3 4 6 7 7 10 11 12

The Newborn Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neonatal Seizures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EEG Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 1.6.2 Physiological Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . Extraphysiological Artifacts . . . . . . . . . . . . . . . . . . . . . . . .

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1.7 1.8 1.9

Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scope of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14 16 16

2 Background Study 2.1 2.2 2.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of this Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gotman et al. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 2.3.2 2.3.3 2.4 General method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adjustments to the algorithm . . . . . . . . . . . . . . . . . . . . . . . . Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 19 20 21 21 24 25 30 30 32 33 34 34 39 41 42 43

The Liu et al. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 2.4.2 2.4.3 General method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adjustments to the algorithm . . . . . . . . . . . . . . . . . . . . . . . . Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.5

The Celka and Colditz Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 2.5.2 2.5.3 General method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adjustments to the algorithm . . . . . . . . . . . . . . . . . . . . . . . . Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.6 2.7

Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2.8 2.9

Other Previously Published Methods . . . . . . . . . . . . . . . . . . . . . . . . The Proposed Neonatal Seizure Detection System . . . . . . . . . . . . . . . . . 2.9.1 2.9.2 2.9.3 Artifact Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 47 48 49 49 50 51

2.10 Development Data Set