Signal Processing for Medical Applications …...Niedermeyer E, lopes da silva F. Electroencephalography- Basic principles, clinical applications, and related fields. Lippincott Williams

Muthuraman Muthuraman Christian-Albrechts-Universität zu Kiel Department of Neurology / Faculty of Engineering Digital Signal Processing and System Theory

Signal Processing for Medical Applications –

Frequency Domain Analyses

Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-2

1.Basics of Brain –

i) Brain signals - EEG/ MEG;

ii) Muscle signals - EMG;

iii) Magnetic resonance imaging – MRI

iv) Tremor disorders

2. Quantities measured from time series in frequency domain

i) Power spectrum

ii) Modelling time series using AR2 processes

ii) Coherence spectrum

- Different windows used for the estimation

iii) Phase spectrum

iv) Delay between signals

3. Source analysis in the frequency domain

- Forward problem

- Inverse problem

- Different Solutions

Lecture 1 & 2

Lecture 3

Lecture 4

Lecture 5

Lecture 6-10

Contents


Books:

EEG:

Niedermeyer E, lopes da silva F. Electroencephalography- Basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.

Sanei S, Chambers J. Introduction to EEG: EEG Signal Processing. John Wiley and Sons Ltd., 2007.

EMG:

Journee HL, van Manen J. Improvement of the detectability of simulated pathological tremour e.m.g.s by means of demodulation and spectral analysis. Med. & Biol. Eng.

& Comput., 1983, 21,587-590

MRI:

M.F. Reiser · W. Semmler · H. Hricak (Eds.). Magnetic resonance tomography. Springer, 2008.

Papers: MEG:

Vrba J, Robinson, SE. Signal processing in Magentoencephalography. Methods 25, 249-271, 2001.

Tremor disorders:

G. Deuschl, J. Raethjen, M. Lindemann, P. Krack. The pathophysiology of Parkinsonian tremor. Muscle Nerve 24, 2001, pp. 716-735.

Deuschl G, Bergman H. Pathophysiology of nonparkinsonian tremors. Mov Disord 2002;17 Suppl 3:S41-8

Books & Papers:


Power, coherence, phase and delay:

D.M. Halliday, J.R. Rosenberg, A.M. Amjad, P. Breeze, B.A. Conway, S.F. Farmer. A frame work for the analysis of mixed time series /point process data-theory and

application to study of physiological tremor, single motor unit discharges and electromyograms. Prog Biophys Mol Bio, 64 (1995), pp. 237–238

T. Muller, M. Lauk, M. Reinhard, A. Hetzel, C.H. Lucking, J. Timmer. Estimation of delay times in biological systems. Ann Biomed Eng, 31 (11) (2003), pp. 1423–1439.

R.B. Govindan, J. Raethjen, F. Kopper, J.C. Claussen, G. Deuschl. Estimation of delay time by coherence analysis. Physica A, 350 (2005), pp. 277–295.

Muthuraman, M.; Govindan, R.B.; Deuschl, G.; Heute, U.; Raethjen.J: Differentiating Phaseshift and Delay in Narrow band Coherent Signals. Clinical Neurophysiology

Journal 119:1062-1070, 2008.

Forward problem:

M. Fuchs, J. Kastner, M. Wagner, S, Hawes, J. S. Ebersole. A standardized boundary element method volume conductor model. Clincal Neurophysiology 113 (5), 2002,

pp.702-712.

Muthuraman, M; Heute, U; Deuschl, G; Raethjen, J; The central oscillatory network of essential tremor. IEEE Proceedings in EMBC, 1: 154-157, 2010.

Inverse problem:

Muthuraman, M; Raethjen, J; Hellriegel, H; Deuschl, G; Heute, U.: Imaging Coherent sources of tremor related EEG activity in patients with Parkinson's disease. IEEE

proceedings in EMBC 4716-4719, Vancouver, Canada, 20.-24.Aug 2008.

Dynamic imaging of coherence sources (DICS) source analysis:

Muthuraman, M; Heute, U; Arning, K; Anwar, AR; Elble, R; Deuschl, G; Raethjen, J.; Oscillating central motor networks in pathological tremors and voluntary

movements. What makes the difference?. Neuroimage, 60(2), 1331-1339, 2012.

Books & Papers:


Non-invasive methods of neuroimaging

Lecture 1 – Basics of Brain


History about EEG

Luigi Galvani: „Animal Electricity“


Franz Anton Mesmer: animal magnetism



Physiological electro-magentic signals



Magentoencephalography



Progress in magentoencephalography



Gamma

Beta

Alpha

Theta

Delta

Brain waves



Electroencephalograhy (EEG)

Electroencephalography is the measurement of electrical

activity produced by the brain as recorded from electrodes

placed on the scalp.



Physiological background of EEG and MEG



Physiological background of EEG and MEG



Generation of magnetic fields



Visual evoked EEG and MEG responses



Secondary

currents

Magnetic

field Dipole

Electroencephalograhy (EEG) & Magnetoencephalography (MEG)



64-Channel EEG Hand Muscles EMG

EMG

Electromyography (EMG) is a technique for evaluating and recording

the activation signal of muscles. The electrical potential generated by

muscle cells when these cells contract, and also when the cells are at

rest.



SQUID



Noise suppression: magentometers and gradiometers



MEG

• In modern day MEG systems we use the superconducting quantum interference

device(SQUID).

• A SQUID is a small (2-3 mm) ring of superconducting material in which one or more

insulating juntions have been made for tunneling the measured magnetic flux by

using a larger pickup coil, known as a, magnetometer, that measures the magnetic

flux over a relatively larger area.

• It is desirable to measure the magnetic field with a high sampling density containing

200-300 separate SQUID detectors distributed over the surface of the head that

allows the measurement of the magnetic field simultaneously at multiple locations

over the whole head.



MEG

• All these detectors with their corresponding pickup coils have to be

immersed in a single liquid helium dewar reservoir, which

maintains the superconducting components at 4.2 ° K.

• It is designed to be used at low temperature in order to reduce

thermal noise and increase mechnical stability.



EEG MEG


Documents

Signal Processing for Medical Applications …...Niedermeyer E, lopes da silva F. Electroencephalography- Basic principles, clinical applications, and related fields. Lippincott Williams