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

324 Pages·2011·17.74 MB·English

by  Katarzyna J. Blinowska

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
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© 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
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© 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
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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
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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