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Signal Processing for Medical Applications Frequency Domain - - PowerPoint PPT Presentation

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Signal Processing for Medical Applications Frequency Domain Analyses Muthuraman Muthuraman Christian-Albrechts-Universitt zu Kiel Department of Neurology / Faculty of Engineering Digital Signal Processing and System Theory Contents

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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

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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

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-3

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:

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-4

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:

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-5

Non-invasive methods of neuroimaging

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-6

History about EEG

Luigi Galvani: „Animal Electricity“

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-7

Franz Anton Mesmer: animal magnetism

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-8

Physiological electro-magentic signals

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-9

Magentoencephalography

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-10

Progress in magentoencephalography

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-11

Gamma Beta Alpha Theta Delta Brain waves

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-12

Electroencephalograhy (EEG) Electroencephalography is the measurement of electrical activity produced by the brain as recorded from electrodes placed on the scalp.

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-13

Physiological background of EEG and MEG

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-14

Physiological background of EEG and MEG

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-15

Generation of magnetic fields

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-16

Visual evoked EEG and MEG responses

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-17

Secondary currents Magnetic field Dipole

Electroencephalograhy (EEG) & Magnetoencephalography (MEG)

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-18

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.

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-19

SQUID

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-20

Noise suppression: magentometers and gradiometers

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-21

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

  • ver the whole head.

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-22

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.

Lecture 1 – Basics of Brain

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Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-23

EEG MEG

Lecture 1 – Basics of Brain