Signal Processing for Medical Applications – Frequency Domain Analyses Muthuraman Muthuraman Christian-Albrechts-Universität zu Kiel Department of Neurology / Faculty of Engineering Digital Signal Processing and System Theory
Contents 1.Basics of Brain – i) Brain signals - EEG/ MEG; ii) Muscle signals - EMG; Lecture 1 & 2 iii) Magnetic resonance imaging – MRI iv) Tremor disorders 2. Quantities measured from time series in frequency domain Lecture 3 i) Power spectrum ii) Modelling time series using AR2 processes ii) Coherence spectrum Lecture 4 - Different windows used for the estimation iii) Phase spectrum Lecture 5 iv) Delay between signals 3. Source analysis in the frequency domain - Forward problem Lecture 6-10 - Inverse problem - Different Solutions Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-2
Books & Papers: 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 Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-3
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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-4
Lecture 1 – Basics of Brain Non-invasive methods of neuroimaging Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-5
Luigi Galvani: „Animal Electricity“ History about EEG Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-6
Lecture 1 – Basics of Brain Franz Anton Mesmer: animal magnetism Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-7
Lecture 1 – Basics of Brain Physiological electro-magentic signals Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-8
Lecture 1 – Basics of Brain Magentoencephalography Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-9
Lecture 1 – Basics of Brain Progress in magentoencephalography Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-10
Lecture 1 – Basics of Brain Brain waves Gamma Beta Alpha Theta Delta Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-11
Lecture 1 – Basics of Brain Electroencephalograhy (EEG) Electroencephalography is the measurement of electrical activity produced by the brain as recorded from electrodes placed on the scalp. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-12
Lecture 1 – Basics of Brain Physiological background of EEG and MEG Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-13
Lecture 1 – Basics of Brain Physiological background of EEG and MEG Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-14
Lecture 1 – Basics of Brain Generation of magnetic fields Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-15
Lecture 1 – Basics of Brain Visual evoked EEG and MEG responses Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-16
Lecture 1 – Basics of Brain Electroencephalograhy (EEG) & Magnetoencephalography (MEG) Secondary currents Magnetic field Dipole Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-17
Lecture 1 – Basics of Brain Hand Muscles EMG 64-Channel EEG 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-18
Lecture 1 – Basics of Brain SQUID Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-19
Lecture 1 – Basics of Brain Noise suppression: magentometers and gradiometers Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-20
Lecture 1 – Basics of Brain 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-21
Lecture 1 – Basics of Brain 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. Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-22
Lecture 1 – Basics of Brain EEG MEG Digital Signal Processing and System Theory| Signal Processing for Medical Applications | Introduction Slide I-23
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