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Practical Biomedical Signal Analysis Using MATLAB® (Series in Medical Physics and Biomedical Engineering) PDF

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Practical Biomedical Signal Analysis Using MATLAB® © 2012 by Taylor & Francis Group, LLC Series in Medical Physics and Biomedical Engineering Series Editors: John G Webster, Slavik Tabakov, Kwan-Hoong Ng Other recent books in the series: Physics for Diagnostic Radiology, Third Edition P P Dendy and B Heaton (Eds) Nuclear Medicine Physics J J Pedroso de Lima (Ed) Handbook of Photonics for Biomedical Science Valery V Tuchin (Ed) Handbook of Anatomical Models for Radiation Dosimetry Xie George Xu and Keith F Eckerman (Eds) Fundamentals of MRI: An Interactive Learning Approach Elizabeth Berry and Andrew J Bulpitt Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues Valery V Tuchin (Ed) Intelligent and Adaptive Systems in Medicine Oliver C L Haas and Keith J Burnham A Introduction to Radiation Protection in Medicine Jamie V Trapp and Tomas Kron (Eds) A Practical Approach to Medical Image Processing Elizabeth Berry Biomolecular Action of Ionizing Radiation Shirley Lehnert An Introduction to Rehabilitation Engineering R A Cooper, H Ohnabe, and D A Hobson The Physics of Modern Brachytherapy for Oncology D Baltas, N Zamboglou, and L Sakelliou Electrical Impedance Tomography D Holder (Ed) Contemporary IMRT S Webb © 2012 by Taylor & Francis Group, LLC Series in Medical Physics and Biomedical Engineering K J Blinowska University of Warsaw, Poland J Zygierewicz University of Warsaw, Poland Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business A TAYLOR & FRANCIS BOOK © 2012 by Taylor & Francis Group, LLC MATLAB® and Simulink® are trademarks of The MathWorks, Inc. and are used with permission. The Math- Works does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® and Simulink® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® and Simulink® software. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2012 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20110823 International Standard Book Number-13: 978-1-4398-1203-7 (eBook - PDF); 978-1-4398-1202-0 (hbk.) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmit- ted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright. com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com © 2012 by Taylor & Francis Group, LLC Contents AbouttheSeries xi Preface xiii Authors xv ListofAbbreviations xvii 1 Introductoryconcepts 1 1.1 Stochasticanddeterministicsignals,conceptsofstationarityander- godicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Discretesignals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Thesamplingtheorem . . . . . . . . . . . . . . . . . . . . 4 1.2.1.1 Aliasing . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Quantizationerror . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Lineartimeinvariantsystems . . . . . . . . . . . . . . . . . . . . 7 1.4 Dualityoftimeandfrequencydomain . . . . . . . . . . . . . . . . 9 1.4.1 Continuousperiodicsignal . . . . . . . . . . . . . . . . . . 10 1.4.2 Infinitecontinuoussignal . . . . . . . . . . . . . . . . . . . 10 1.4.3 Finitediscretesignal . . . . . . . . . . . . . . . . . . . . . 11 1.4.4 BasicpropertiesofFouriertransform . . . . . . . . . . . . 11 1.4.5 Powerspectrum:thePlanchereltheoremandParseval’sthe- orem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4.6 Z-transform . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.7 Uncertaintyprinciple . . . . . . . . . . . . . . . . . . . . . 14 1.5 Hypothesestesting . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.5.1 Thenullandalternativehypothesis . . . . . . . . . . . . . . 15 1.5.2 Typesoftests . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.5.3 Multiplecomparisonproblem . . . . . . . . . . . . . . . . 17 1.5.3.1 Correctingthesignificancelevel. . . . . . . . . . 18 1.5.3.2 Parametricandnonparametricstatisticalmaps . . 19 1.5.3.3 Falsediscoveryrate . . . . . . . . . . . . . . . . 20 1.6 Surrogatedatatechniques . . . . . . . . . . . . . . . . . . . . . . 20 2 Singlechannel(univariate)signal 23 2.1 Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.1.1 Designingfilters . . . . . . . . . . . . . . . . . . . . . . . 25 v © 2012 by Taylor & Francis Group, LLC (cid:2) vi PracticalBiomedicalSignalAnalysisUsingMATLABR 2.1.2 Changingthesamplingfrequency . . . . . . . . . . . . . . 27 2.1.3 Matchedfilters . . . . . . . . . . . . . . . . . . . . . . . . 28 2.1.4 Wienerfilter . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.2 Probabilisticmodels . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.1 HiddenMarkovmodel . . . . . . . . . . . . . . . . . . . . 30 2.2.2 Kalmanfilters . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 Stationarysignals . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.1 Analytictoolsinthetimedomain . . . . . . . . . . . . . . 33 2.3.1.1 Meanvalue,amplitudedistributions . . . . . . . . 33 2.3.1.2 Entropyandinformationmeasure . . . . . . . . . 34 2.3.1.3 Autocorrelationfunction . . . . . . . . . . . . . . 34 2.3.2 Analytictoolsinthefrequencydomain . . . . . . . . . . . 35 2.3.2.1 Estimators of spectral power density based on Fouriertransform . . . . . . . . . . . . . . . . . 35 2.3.2.1.1 Choiceofwindowingfunction . . . . . 36 2.3.2.1.2 ErrorsofFourierspectralestimate . . . 37 2.3.2.1.3 Relationofspectraldensityandtheau- tocorrelationfunction . . . . . . . . . . 39 2.3.2.1.4 Bispectrumandbicoherence . . . . . . 39 2.3.2.2 Parametricmodels:AR,ARMA . . . . . . . . . . 40 2.3.2.2.1 ARmodelparameterestimation . . . . 41 2.3.2.2.2 ChoiceoftheARmodelorder . . . . . 42 2.3.2.2.3 ARmodelpowerspectrum . . . . . . . 42 2.3.2.2.4 Parametric description of the rhythms byARmodel,FADmethod . . . . . . . 45 2.4 Non-stationarysignals . . . . . . . . . . . . . . . . . . . . . . . . 47 2.4.1 Instantaneousamplitudeandinstantaneousfrequency . . . . 47 2.4.2 Analytictoolsinthetime-frequencydomain . . . . . . . . 48 2.4.2.1 Time-frequencyenergydistributions . . . . . . . 48 2.4.2.1.1 Wigner-Villedistribution . . . . . . . . 49 2.4.2.1.2 Cohenclass . . . . . . . . . . . . . . . 50 2.4.2.2 Time-frequencysignaldecompositions . . . . . . 52 2.4.2.2.1 Short time Fourier transform and spec- trogram . . . . . . . . . . . . . . . . . 52 2.4.2.2.2 Continuouswavelettransformandscalo- gram . . . . . . . . . . . . . . . . . . . 54 2.4.2.2.3 Discretewavelettransform . . . . . . . 56 2.4.2.2.4 Dyadicwavelettransform—multiresolution signaldecomposition . . . . . . . . . . 56 2.4.2.2.5 Waveletpackets . . . . . . . . . . . . . 59 2.4.2.2.6 WaveletsinMATLAB . . . . . . . . . 60 2.4.2.2.7 Matchingpursuit—MP . . . . . . . . . 60 2.4.2.2.8 Comparisonoftime-frequencymethods 63 2.4.2.2.9 EmpiricalmodedecompositionandHilbert- Huangtransform . . . . . . . . . . . . 65 © 2012 by Taylor & Francis Group, LLC Contents vii 2.5 Non-linearmethodsofsignalanalysis . . . . . . . . . . . . . . . . 66 2.5.1 Lyapunovexponent . . . . . . . . . . . . . . . . . . . . . . 67 2.5.2 Correlationdimension . . . . . . . . . . . . . . . . . . . . 68 2.5.3 Detrendedfluctuationanalysis . . . . . . . . . . . . . . . . 69 2.5.4 Recurrenceplots . . . . . . . . . . . . . . . . . . . . . . . 70 2.5.5 Poincare´map . . . . . . . . . . . . . . . . . . . . . . . . . 72 2.5.6 Approximateandsampleentropy. . . . . . . . . . . . . . . 72 2.5.7 Limitationsofnon-linearmethods . . . . . . . . . . . . . . 73 3 Multiplechannels(multivariate)signals 75 3.1 Cross-estimators: cross-correlation, cross-spectra, coherence (ordi- nary,partial,multiple) . . . . . . . . . . . . . . . . . . . . . . . . 75 3.2 Multivariateautoregressivemodel(MVAR) . . . . . . . . . . . . . 77 3.2.1 FormulationofMVARmodel . . . . . . . . . . . . . . . . 77 3.2.2 MVARinthefrequencydomain . . . . . . . . . . . . . . . 79 3.3 Measuresofdirectedness . . . . . . . . . . . . . . . . . . . . . . . 80 3.3.1 Estimatorsbasedonthephasedifference. . . . . . . . . . . 80 3.3.2 Causalitymeasures . . . . . . . . . . . . . . . . . . . . . . 81 3.3.2.1 Grangercausality . . . . . . . . . . . . . . . . . 81 3.3.2.2 Grangercausalityindex . . . . . . . . . . . . . . 82 3.3.2.3 Directedtransferfunction . . . . . . . . . . . . . 82 3.3.2.3.1 dDTF . . . . . . . . . . . . . . . . . . 84 3.3.2.3.2 SDTF . . . . . . . . . . . . . . . . . . 85 3.3.2.4 Partialdirectedcoherence . . . . . . . . . . . . . 85 3.4 Non-linearestimatorsofdependenciesbetweensignals . . . . . . 87 3.4.1 Non-linearcorrelation . . . . . . . . . . . . . . . . . . . . 87 3.4.2 Kullback-Leibler entropy, mutual information and transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.4.3 Generalizedsynchronization . . . . . . . . . . . . . . . . . 89 3.4.4 Phasesynchronization . . . . . . . . . . . . . . . . . . . . 89 3.4.5 Testingthereliabilityoftheestimatorsofdirectedness . . . 90 3.5 Comparisonofthemultichannelestimatorsofcouplingbetweentime series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.6 Multivariatesignaldecompositions . . . . . . . . . . . . . . . . . 95 3.6.1 Principalcomponentanalysis(PCA) . . . . . . . . . . . . . 95 3.6.1.1 Definition . . . . . . . . . . . . . . . . . . . . . 95 3.6.1.2 Computation . . . . . . . . . . . . . . . . . . . . 96 3.6.1.3 Possibleapplications . . . . . . . . . . . . . . . . 96 3.6.2 Independentcomponentsanalysis(ICA) . . . . . . . . . . . 97 3.6.2.1 Definition . . . . . . . . . . . . . . . . . . . . . 97 3.6.2.2 Estimation . . . . . . . . . . . . . . . . . . . . . 98 3.6.2.3 Computation . . . . . . . . . . . . . . . . . . . . 98 3.6.2.4 Possibleapplications . . . . . . . . . . . . . . . . 99 3.6.3 Multivariatematchingpursuit(MMP) . . . . . . . . . . . . 99 © 2012 by Taylor & Francis Group, LLC (cid:2) viii PracticalBiomedicalSignalAnalysisUsingMATLABR 4 Applicationtobiomedicalsignals 101 4.1 Brain signals: local field potentials (LFP), electrocorticogram (ECoG),electroencephalogram(EEG),andmagnetoencephalogram (MEG),event-relatedresponses(ERP),andevokedfields(EF) . . . 101 4.1.1 Generationofbrainsignals . . . . . . . . . . . . . . . . . . 103 4.1.2 EEG/MEGrhythms . . . . . . . . . . . . . . . . . . . . . 105 4.1.3 EEGmeasurement,electrodesystems . . . . . . . . . . . . 107 4.1.4 MEGmeasurement,sensorsystems . . . . . . . . . . . . . 109 4.1.5 Eliminationofartifacts . . . . . . . . . . . . . . . . . . . . 109 4.1.6 AnalysisofcontinuousEEGsignals . . . . . . . . . . . . . 115 4.1.6.1 Singlechannelanalysis . . . . . . . . . . . . . . 116 4.1.6.2 Multiplechannelanalysis . . . . . . . . . . . . . 117 4.1.6.2.1 Mapping. . . . . . . . . . . . . . . . . 117 4.1.6.2.2 MeasuringofdependencebetweenEEG signals . . . . . . . . . . . . . . . . . . 118 4.1.6.3 SleepEEGanalysis . . . . . . . . . . . . . . . . 122 4.1.6.4 AnalysisofEEGinepilepsy . . . . . . . . . . . 129 4.1.6.4.1 Quantificationofseizures . . . . . . . . 130 4.1.6.4.2 Seizuredetectionandprediction . . . . 133 4.1.6.4.3 Localizationofanepilepticfocus. . . . 137 4.1.6.5 EEGinmonitoringandanesthesia. . . . . . . . . 138 4.1.6.5.1 Monitoringbrain injury by quantitative EEG . . . . . . . . . . . . . . . . . . . 138 4.1.6.5.2 MonitoringofEEGduringanesthesia . 138 4.1.7 AnalysisofepochedEEGsignals . . . . . . . . . . . . . . 139 4.1.7.1 Analysisofphaselockedresponses . . . . . . . . 141 4.1.7.1.1 Timeaveraging . . . . . . . . . . . . . 141 4.1.7.1.2 Influenceofnoisecorrelation . . . . . 143 4.1.7.1.3 Variationsinlatency . . . . . . . . . . 143 4.1.7.1.4 Habituation . . . . . . . . . . . . . . . 144 4.1.7.2 Inpursuitofsingletrialevokedresponses . . . . . 145 4.1.7.2.1 Wienerfilters . . . . . . . . . . . . . . 145 4.1.7.2.2 Modelbasedapproach . . . . . . . . . 145 4.1.7.2.3 Time-frequencyparametricmethods . . 146 4.1.7.2.4 ERPtopography. . . . . . . . . . . . . 147 4.1.7.3 Analysisofnon-phaselockedresponses. . . . . . 150 4.1.7.3.1 Event-relatedsynchronizationanddesyn- chronization . . . . . . . . . . . . . . . 150 4.1.7.3.2 Classicalfrequencybandmethods . . . 151 4.1.7.3.3 Time-frequencymethods . . . . . . . . 153 4.1.7.3.4 ERD/ERSinthestudyofiEEG . . . . . 156 4.1.7.3.5 Event-related time-varying functional connectivity . . . . . . . . . . . . . . . 158 4.1.7.3.6 Functionalconnectivityestimationfrom intracranialelectricalactivity . . . . . . 163 © 2012 by Taylor & Francis Group, LLC Contents ix 4.1.7.3.7 Statistical assessment of time-varying connectivity . . . . . . . . . . . . . . . 166 4.1.8 MultimodalintegrationofEEGandfMRIsignals . . . . . . 167 4.2 Heartsignals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 4.2.1 Electrocardiogram . . . . . . . . . . . . . . . . . . . . . . 169 4.2.1.1 Measurementstandards . . . . . . . . . . . . . . 169 4.2.1.2 Physiological background and clinical applica- tions . . . . . . . . . . . . . . . . . . . . . . . . 170 4.2.1.3 ProcessingofECG . . . . . . . . . . . . . . . . . 173 4.2.1.3.1 Artifactremoval. . . . . . . . . . . . . 173 4.2.1.3.2 MorphologicalECGfeatures . . . . . . 175 4.2.1.3.3 Spatial representation of ECG activ- ity;bodysurfacepotentialmappingand vectorcardiography . . . . . . . . . . . 176 4.2.1.3.4 StatisticalmethodsandmodelsforECG analysis . . . . . . . . . . . . . . . . . 178 4.2.1.3.5 ECGpatternsclassification . . . . . . . 179 4.2.2 Heartratevariability . . . . . . . . . . . . . . . . . . . . . 180 4.2.2.1 Time-domainmethodsofHRVanalysis . . . . . . 180 4.2.2.2 Frequency-domainmethodsofHRVanalysis . . . 181 4.2.2.3 RelationofHRVtoothersignals . . . . . . . . . 183 4.2.2.4 Non-linearmethodsofHRVanalysis . . . . . . . 184 4.2.2.4.1 Empiricalmodedecomposition . . . . . 185 4.2.2.4.2 Entropymeasures . . . . . . . . . . . . 186 4.2.2.4.3 Detrendedfluctuationanalysis . . . . . 187 4.2.2.4.4 Poincare´andrecurrenceplots. . . . . . 188 4.2.2.4.5 Effectivenessofnon-linearmethods . . 189 4.2.3 FetalECG . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 4.2.4 Magnetocardiogramandfetalmagnetocardiogram. . . . . . 195 4.2.4.1 Magnetocardiogram . . . . . . . . . . . . . . . . 195 4.2.4.2 FetalMCG . . . . . . . . . . . . . . . . . . . . . 199 4.3 Electromyogram . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 4.3.1 Measurementtechniquesandphysiologicalbackground. . . 201 4.3.2 QuantificationofEMGfeatures . . . . . . . . . . . . . . . 205 4.3.3 DecompositionofneedleEMG . . . . . . . . . . . . . . . . 206 4.3.4 SurfaceEMG . . . . . . . . . . . . . . . . . . . . . . . . . 210 4.3.4.1 SurfaceEMGdecomposition . . . . . . . . . . . 211 4.4 Gastro-intestinalsignals . . . . . . . . . . . . . . . . . . . . . . . 218 4.5 Acousticsignals . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 4.5.1 Phonocardiogram . . . . . . . . . . . . . . . . . . . . . . . 221 4.5.2 Otoacousticemissions . . . . . . . . . . . . . . . . . . . . 224 References 231 Index 291 © 2012 by Taylor & Francis Group, LLC

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Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and
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