Multifractals and Chronic Diseases of the Central Nervous System

198 Pages·2019·5.306 MB·English

by  Dipak Ghosh, Shukla Samanta, Sayantan Chakraborty

Dipak Ghosh · Shukla Samanta 
Sayantan Chakraborty
Multifractals and
Chronic Diseases
of the Central
Nervous System
Multifractals and Chronic Diseases of the Central
Nervous System
(cid:129) (cid:129)
Dipak Ghosh Shukla Samanta
Sayantan Chakraborty
Multifractals and Chronic
Diseases of the Central
Nervous System
DipakGhosh ShuklaSamanta
DepartmentofPhysics DepartmentforPhysics
SirCVRamanCentreforPhysicsand SeacomEngineeringCollege
Music,JadavpurUniversity Howrah,WestBengal,India
Kolkata,WestBengal,India
SayantanChakraborty
ElectricalandElectronicsEngineering
ICFAIUniversity
Agartala,Tripura,India
ISBN978-981-13-3551-8 ISBN978-981-13-3552-5 (eBook)
https://doi.org/10.1007/978-981-13-3552-5
LibraryofCongressControlNumber:2018964106
©SpringerNatureSingaporePteLtd.2019
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Preface
“Neurodegeneration” corresponds to any pathological condition which primarily
affectsneurons.Neurologicaldisordersnotonlyaffectthebrainbutalsothenerves
that are found throughout the body and spinal cord. Neurodegenerative diseases
(NDDs) are defined as disorders that affect the central nervous system causing
progressive dysfunction of the nervous system. These incurable and exhausting
conditions are characterized by loss of neuronal cell function and are often associ-
atedwiththedeteriorationofstructuresoftheaffectednervoussystem.Diagnosisof
neurologicaldiseasesisagrowingconcernandoneofthemostdifficultchallenges
for modernmedicine. According totheWorldHealthOrganization’srecentreport,
neurological disorders, such as epilepsy, Alzheimer’s disease, Parkinson’s disease,
Huntington’s disease (HD), stroke, and headache, to name a few, affect up to one
billionpeopleworldwide.Anestimated6.8millionpeopledieeveryyearasaresult
ofneurologicaldisorders.
Thebookprimarilyfocusesonthestudy ofdifferentneurologicaldisorders like
epilepsy,Alzheimer’s,Parkinson’s,HD,andmotorneurondiseases(MNDs)froma
new perspective by analyzing the physiological signals such as EEG, EMG, ECG,
andgaitrhythmassociatedwiththesediseasesusingnonlineardynamics.
Physiological signals such as heart rate, blood pressure, respiration, and stride
intervalsfluctuatecontinuouslyovertimereflectingthecomplexregulationofthese
signals by the central nervous system. Analyzing the dynamics of these human
physiological signals is an important area of research to help control and to be
abletopredict theonsetofpathological conditions.Since long,severalresearchers
haveuseddifferenttechniques,mostlylinear,forstudyingvariousdiseases,but,of
late,researchonnerve-relateddiseasesordisordershasgainedmuchimportanceas
theworldsuffersalotofdeathsduetotheseprogressiveneurondiseaseswhicharea
slowandsilentkillerduetothelackofproperknowledgeandappropriatemedica-
tion. Since we have been working on various neurological disorders for more than
10years,wedecidedtosummarizeourworksandalsotheworksofotherresearchers
inthisfieldintheformofabook.
v
vi Preface
Like every other system found in nature, physiological signals are also of
complex character, as they are composed of many subsystems which are strongly
correlatedtoeachother,butnotinalinearfashion.Conventionallineartechniques
like amplitude, root mean square, or Fourier analysis cannot provide detailed
information about these subsystems. The development of nonlinear methods has
significantly helped in studying complex nonlinear systems in detail by providing
accurateandpreciseinformationaboutthem.Nonlineartimeseriesanalysismethods
enablethedeterminationofcharacteristicquantitiesofaparticularsystemsolelyby
analyzing the time course of one of its variables. Thus, from this viewpoint,
nonlinear time series analysis methods are superior to mathematical modeling,
sincethey enable theintroductionofbasicconceptsdirectlyfrom theexperimental
data. Since works of several researchers have established the complex nonlinear
characterofphysiologicalsignalslikeEEG,ECG,EMG,andhumangaitrhythm,we
havebeenmotivatedtousenonlineartechniquesinourwork.
Inanutshell,thebookprovidesacomprehensivestudyonmostoftheneurolog-
icaldisorderswithspecialemphasisonthemethodsusedwhicharenotonlynewbut
also rigorous and robust. The findings provide simple parameters for the diagnosis
and prognosis of different neurodegenerative disorders, and adequate software can
bedevelopedwhichcaneasilybecoupledwithmachines.Theoverallpremiseofthe
bookforanalyzingbioelectricalsignalsusingnonlineartechniquesiseasilyachiev-
ableandneedoftheday.Thebookwillbeeasilyaccessibleandusefultoaverylarge
communityworkinginbiomedicalsciencesandengineering.
We hope that this compilation of the original research work on the analysis of
brain through fractal analysis will definitely provide a platform and a direction for
inquisitivestudentsandresearchersofbiomedicalengineeringandneuroscientiststo
think objectively on the premises. We also feel that this work will stimulate more
exhaustive research on different other neurological disorders which are not com-
monlystudied.
We are really happy that the leading publisher Springer Nature has accepted to
publishthebook.WesincerelythanktheeditorsandalltheotherstaffsofSpringer
Naturefortheircontinualhelp,support,andsuggestions.
Kolkata,WestBengal,India DipakGhosh
Howrah,WestBengal,India ShuklaSamanta
Agartala,Tripura,India SayantanChakraborty
Acknowledgment
We begin this acknowledgment section with Albert Einstein’s philosophical
thought:
ManytimesadayIrealizehowmuchmyownouterandinnerlifeisbuiltuponthelaborsof
myfellowmen,bothlivinganddead,andhowearnestlyImustexertmyselfinordertogive
inreturnasmuchasIhavereceived.
We would like to express our deepest appreciation to all brilliant authors,
researchers, and doctors who have provided us with a wealth of wisdom in the
domain of diseases of the central nervous system. We believe that the analysis of
biomedical signal with a novel new approach yielding precise results has become
possible due to a breakthrough idea of Mandelbrot introducing a new concept of
“fractals.” We take this opportunity to record our acknowledgment to Benoit
Mandelbrot.
WewouldliketoexpressourheartiestthankstoSpringerNatureandtheeditors
fortheirkindsupportandencouragementwhichhavehelpedusinthecompletionof
thebook.Wewouldalsoliketoexpressourgratitudetowardthereviewersfortheir
kindcooperationandimportantsuggestionswhichhavehelpedtoenrichthebook.
Wealsowanttoextendourthanksandappreciationtoalltheotherstaffmembersof
SpringerNaturefortheircontinualsupportandsuggestions.
vii
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 CentralNervousSystemandItsDiseases. . . . . . . . . . . . . . . . . . 1
1.2 BioelectricalSignals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Electroencephalography. . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Electrocardiography. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.3 Electromyography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 LinearSignalProcessingTechniques. . . . . . . . . . . . . . . . . . . . . 9
1.3.1 RootMeanSquare(RMS)Method. . . . . . . . . . . . . . . . . 10
1.3.2 FastFourierTransform(FFT)Method. . . . . . . . . . . . . . . 10
1.3.3 Short-TimeFourierTransform(STFT)Method. . . . . . . . 10
1.3.4 WaveletTransform(WT)Method. . . . . . . . . . . . . . . . . . 11
1.3.5 DiscreteWaveletTransform(DWT)Method. . . . . . . . . . 11
1.4 LimitationsofLinearAnalysisTechniques. . . . . . . . . . . . . . . . . 11
1.5 Non-linearTechniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.5.1 FractalsandMultifractals. . . . . . . . . . . . . . . . . . . . . . . . 13
1.5.2 Non-linearAnalysisofBiomedicalSignals. . . . . . . . . . . 22
1.6 ReviewofStudiesonNeurologicalDisorders. . . . . . . . . . . . . . . 26
1.6.1 Epilepsy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.6.2 Dementia(Alzheimer’sDisease). . . . . . . . . . . . . . . . . . . 27
1.6.3 Parkinson’sDisease. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.6.4 Huntington’sDisease. . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.6.5 MotorNeuronDisease(MND). . . . . . . . . . . . . . . . . . . . 30
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2 MultifractalStudyofEEGSignalofSubjectswith
EpilepsyandAlzheimer’s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.2 NeurologicalDisorder:Epilepsy,Alzheimer’s,andEEGData. . . 48
2.2.1 Epilepsy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.2.2 Alzheimer’sDisease. . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.2.3 EEGData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
ix
x Contents
2.3 MultifractalDetrendedFluctuationAnalysisofEEGSignals. . . . 57
2.4 MultifractalDetrendedCross-CorrelationAnalysisof
EEGSignals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.5 PossibleApplicationasBiomarkerofEpilepsy. . . . . . . . . . . . . . 70
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3 MultifractalApproachforQuantificationofAutonomic
DeregulationDuetoEpilepticSeizurewithECGData. . . . . . . . . . . 79
3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.2 SystematicStudiesonAbnormalitiesinCardiac
AutonomicStatus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.3 MultifractalDetrendedFluctuationAnalysisofECG
Signals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.3.1 ECGData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.4 ResultsandPossibleBiomarker. . . . . . . . . . . . . . . . . . . . . . . . . 91
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4 MultifractalAnalysisofElectromyographyData. . . . . . . . . . . . . . . 97
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.2 MotorNeuronandMusculoskeletalDisease:Neuropathy
andMyopathy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3 Electromyography–ATooltoDetectMotorNeuronDisease. . . 98
4.4 StudyofSEMGSignals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.5 ElectromyographyData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.6 MultifractalDetrendedFluctuationAnalysisofEMGSignals. . . . 103
4.7 ResultsandPossibleAdvancedLevelBiomarker. . . . . . . . . . . . 111
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5 MultifractalStudyofParkinson’sandHuntington’sDiseases
withHumanGaitData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.2 Parkinson’sDiseaseandGaitData. . . . . . . . . . . . . . . . . . . . . . . 120
5.2.1 GaitData. . .. . . . .. . . .. . . . .. . . .. . . . .. . . . .. . . .. 121
5.3 MultifractalandMultifractalCross-CorrelationAnalysis
ofParkinson’sDisease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.4 HuntingtonDiseaseandGaitData. . . . . . . . . . . . . . . . . . . . . . . 139
5.5 MultifractalAnalysisofHuntington’sData. . . . . . . . . . . . . . . . . 140
5.6 DiscussionsonPossibleUseoftheResultforBiomarkers
ofParkinson’sandHuntington’s. . . . . . . . . . . . . . . . . . . . . . . . 142
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6 MultifractalCorrelationStudyBetweenPostureandAutonomic
DeregulationUsingECGandBloodPressureData. . . . . . . . . . . . . . 149
6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.1.1 BloodPressure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.1.2 Non-linearHeartRateVariabilityAnalysis. . . . . . . . . . . 152
6.1.3 CorrelationofHeartRateandBloodPressure
Signals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Contents xi
6.2 Posture-DependentECGandArterialBloodPressure
(ABP)Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
6.3 MultifractalCross-CorrelationAnalysisBetweenECG
andABPData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
6.4 DiscussionoftheResultandPossibleUseasBiomarker
ofNeurologicalDisorder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Appendices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173