Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

298 Pages·2021·52.427 MB·English

by  Paolo Castiglioni (editor), Luca Faes (editor), Gaetano Valenza (editor)

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Signals Analysis
Edited by
Paolo Castiglioni, Luca Faes and Gaetano Valenza
Printed Edition of the Special Issue Published in Entropy
www.mdpi.com/journal/entropy
Assessing Complexity in Physiological
Systems through Biomedical Signals
Analysis

Assessing Complexity in Physiological
Systems through Biomedical Signals
Analysis
Editors
PaoloCastiglioni
LucaFaes
GaetanoValenza
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade•Manchester•Tokyo•Cluj•Tianjin
Editors
PaoloCastiglioni LucaFaes GaetanoValenza
IRCCSFondazioneDonCarlo DepartmentofEngineering, DepartmentofInformation
Gnocchi UniversityofPalermo EngineeringandResearch
Italy Italy Center“E.Piaggio”,
UniversityofPisa
Italy
EditorialOffice
MDPI
St.Alban-Anlage66
4052Basel,Switzerland
This is a reprint of articles from the Special Issue published online in the open access journal
Entropy (ISSN 1099-4300) (available at: https://www.mdpi.com/journal/entropy/specialissues/
BiomedicalSignals).
Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas
indicatedbelow:
LastName,A.A.; LastName,B.B.; LastName,C.C.ArticleTitle. JournalNameYear,ArticleNumber,
PageRange.
ISBN 978-3-03943-368-1 (Hbk)
ISBN 978-3-03943-369-8 (PDF)
CoverimagecourtesyofPaoloCastiglioni.
(cid:2)c 2020bytheauthors. ArticlesinthisbookareOpenAccessanddistributedundertheCreative
Commons Attribution (CC BY) license, which allows users to download, copy and build upon
publishedarticles,aslongastheauthorandpublisherareproperlycredited,whichensuresmaximum
disseminationandawiderimpactofourpublications.
ThebookasawholeisdistributedbyMDPIunderthetermsandconditionsoftheCreativeCommons
licenseCCBY-NC-ND.
Contents
AbouttheEditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
PaoloCastiglioni,LucaFaesandGaetanoValenza
AssessingComplexityinPhysiologicalSystemsthroughBiomedicalSignalsAnalysis
Reprintedfrom:Entropy2020,22,1005,doi:10.3390/e22091005 . . . . . . . . . . . . . . . . . . . . 1
JieSun,BinWang,YanNiu,YuanTan,ChanjuanFan,NanZhang,JiayueXue,JingWeiand
JieXiang
ComplexityAnalysisofEEG,MEG,andfMRIinMildCognitiveImpairmentandAlzheimer’s
Disease:AReview
Reprintedfrom:Entropy2020,22,239,doi:10.3390/e22020239. . . . . . . . . . . . . . . . . . . . . 5
SusannaRampichini,TaianMartinsVieira,PaoloCastiglioniandGiampieroMerati
ComplexityAnalysisofSurfaceElectromyographyforAssessingtheMyoelectricManifestation
ofMuscleFatigue:AReview
Reprintedfrom:Entropy2020,22,529,doi:10.3390/e22050529. . . . . . . . . . . . . . . . . . . . . 27
Sebastian Z˙urek, Waldemar Grabowski, Klaudia Wojtiuk, Dorota Szewczak, Przemysław
GuzikandJarosławPiskorski
RelativeConsistencyofSampleEntropyIsNotPreservedinMIXProcesses
Reprintedfrom:Entropy2020,22,694,doi:10.3390/e22060694. . . . . . . . . . . . . . . . . . . . . 59
LinaZhao,JianqingLi,JinleXiong,XueyuLiangandChengyuLiu
Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based
SampleEntropy
Reprintedfrom:Entropy2020,22,411,doi:10.3390/e22040411. . . . . . . . . . . . . . . . . . . . . 69
L.Velasquez-Martinez,J.Caicedo-AcostaandG.Castellanos-Dominguez
Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery
UsingVector-QuantizedPatterns
Reprintedfrom:Entropy2020,22,703,doi:10.3390/e22060703. . . . . . . . . . . . . . . . . . . . . 85
YuriAntonacci,LauraAstolfi,GiandomenicoNolloandLucaFaes
InformationTransferinLinearMultivariateProcessesAssessedthroughPenalizedRegression
Techniques:ValidationandApplicationtoPhysiologicalNetworks
Reprintedfrom:Entropy2020,22,732,doi:10.3390/e22070732. . . . . . . . . . . . . . . . . . . . . 103
DanutaMakowiecandJoannaWdowczyk
Patterns of Heart Rate Dynamics in Healthy Aging Population: Insights from Machine
LearningMethods
Reprintedfrom:Entropy2019,21,1206,doi:10.3390/e21121206 . . . . . . . . . . . . . . . . . . . . 135
Joa˜oMonteiro-Santos,TeresaHenriques,IneˆsNunes,Ce´liaAmorim-Costa,Joa˜oBernardes
andCristinaCosta-Santos
ComplexityofCardiotocographicSignalsasAPredictorofLabor
Reprintedfrom:Entropy2020,22,104,doi:10.3390/e22010104. . . . . . . . . . . . . . . . . . . . . 157
Ming-XiaXiao,Chang-HuaLu,NaTa,Wei-WeiJiang,Xiao-JingTangandHsien-TsaiWu
ApplicationofaSpeedyModifiedEntropyMethodinAssessingtheComplexityofBaroreflex
SensitivityforAge-ControlledHealthyandDiabeticSubjects
Reprintedfrom:Entropy2019,21,894,doi:10.3390/e21090894. . . . . . . . . . . . . . . . . . . . . 169
v
AndreaFaini,SergioCaravita,GianfrancoParatiandPaoloCastiglioni
Alterations of Cardiovascular Complexity during Acute Exposure to High Altitude: A
MultiscaleEntropyApproach
Reprintedfrom:Entropy2019,21,1224,doi:10.3390/e21121224 . . . . . . . . . . . . . . . . . . . . 185
Jacques-OlivierFortrat
Zipf’sLawofVasovagalHeartRateVariabilitySequences
Reprintedfrom:Entropy2020,22,413,doi:10.3390/e22040413. . . . . . . . . . . . . . . . . . . . . 201
PaoloCastiglioni,StefanoOmboni,GianfrancoParatiandAndreaFaini
DayandNightChangesofCardiovascularComplexity:AMulti-FractalMulti-ScaleAnalysis
Reprintedfrom:Entropy2020,22,462,doi:10.3390/e22040462. . . . . . . . . . . . . . . . . . . . . 209
YanbingJiaandHuaguangGu
Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional
NetworksoftheBrain
Reprintedfrom:Entropy2019,21,1156,doi:10.3390/e21121156 . . . . . . . . . . . . . . . . . . . . 227
AmeerGhouse,MimmaNardelliandGaetanoValenza
fNIRSComplexityAnalysisfortheAssessmentofMotorImageryandMentalArithmeticTasks
Reprintedfrom:Entropy2020,22,761,doi:10.3390/e22070761. . . . . . . . . . . . . . . . . . . . . 245
VeroniqueDeschodt-Arsac,EstelleBlons,PierreGilfriche,BeatriceSpiluttiniandLaurent
M.Arsac
Entropy in Heart Rate Dynamics Reflects How HRV-Biofeedback Training Improves
NeurovisceralComplexityduringStress-CognitionInteractions
Reprintedfrom:Entropy2020,22,317,doi:10.3390/e22030317. . . . . . . . . . . . . . . . . . . . . 261
EstelleBlons,LaurentM.Arsac,PierreGilfricheandVeroniqueDeschodt-Arsac
Multiscale Entropy of Cardiac and Postural Control Reflects a Flexible Adaptation to
aCognitiveTask
Reprintedfrom:Entropy2019,21,1024,doi:10.3390/e21101024 . . . . . . . . . . . . . . . . . . . . 275
vi
About the Editors
Paolo Castiglioni (Ph.D.) is a senior researcher at the Fondazione Don Carlo Gnocchi in
Milan (Italy), where he leads the Laboratory of Biosignal Analysis at the Biomedical Technology
Department, andcontractprofessorof“MathematicalMethods”and“PhysicsAppliedtoBiology
and Medicine” at the “Universita` degli Studi di Milano”. He received a degree in Electronic
Engineering(1987)andaPh.D.inBiomedicalEngineering(1993)fromthe“PolitecnicodiMilano”
University(Milan). Hisresearchactivitiesconcerntheanalysisofcardiovascularsignals,EMG,and
EEG;thedevelopmentofalgorithmsforbiosignalanalysis,includingadvancedspectraltechniques,
time-frequencydistributions, andcomplexity-basedmethods. Hisresearchinterestsalsofocuson
modeling humoral and neural mechanisms for the control of the cardiovascular system; portable
systems for long-term monitoring of physiological signals; gravitational and sleep physiology.
He is the author of more than 200 scientific contributions in peer-reviewed journals, conference
proceedings,andbookchapters.
LucaFaes(AssociateProfessor, h-index=41, Scholar)receivedhisMSandPh.D.inElectronic
EngineeringattheUniversityofPadova(1998)andtheUniversityofTrento(2003),respectively. He
waswiththeDept. ofPhysics(2004–2013)andtheBIOtechCenter(2008–2013)oftheUniversityof
Trento,andwiththeBrunoKesslerFoundation(FBK,Trento,2013–2017).Since2018,heisaProfessor
ofBiomedicalEngineeringattheUniversityofPalermo. HehasbeenavisitingscientistattheState
UniversityofNewYork(2007),WorcesterPolytechnicInstitute(2010),UniversityofGent(Belgium,
2013),UniversityofMinasGerais(Brazil,2015),andBostonUniversity(2016).HeisaSeniorMember
oftheIEEE,andamemberoftheTechnicalCommitteeoftheEngineeringinMedicineandBiology
Society (IEEE-EMBS). He serves as an editor for several peer-review journals, including Entropy,
FrontiersinPhysiology,andComputationalandMathematicalMethodsinMedicine. Histeaching
activity includes biosensors, biomedical devices, and biomedical signal processing. His research
activityisfocusedonthedevelopmentofmethodsformultivariatetimeseriesanalysisandsystem
modeling,withapplicationstocardiovascularneuroscience,cardiacarrhythmias,brainconnectivity,
andnetworkphysiology.Furtherdetailscanbefoundatwww.lucafaes.net.
GaetanoValenza(M.Eng., Ph.D.)iscurrentlyanAssistantProfessorofBioengineeringatthe
UniversityofPisa,Pisa,Italy.Hisresearchinterestsincludestatisticalandnonlinearbiomedicalsignal
and image processing, cardiovascular and neural modeling, physiologically-interpretable artificial
intelligencesystems,andwearablesystemsforphysiologicalmonitoring.Applicationsofhisresearch
includetheassessmentofautonomicnervoussystemactivityoncardiovascularcontrol,brain–heart
interactions, affective computing, assessment of mood, and mental/neurological disorders. He
is the author of more than 200 international scientific contributions in these fields published in
peer-reviewedinternationaljournals,conferenceproceedings,books,andbookchapters,andisan
officialreviewerformorethansixtyinternationalscientificjournalsandresearchfundingagencies.
HehasbeeninvolvedinseveralinternationalresearchprojectsandisaSeniorMemberoftheIEEE,
andaMemberoftheIEEETechnicalCommitteeonCardiopulmonarySystems.Dr.Valenzahasbeen
aguesteditorandassociateeditorofseveralinternationalscientificjournals. Furtherdetailscanbe
foundathttp://www.centropiaggio.unipi.it/valenza.
vii

entropy
Editorial
Assessing Complexity in Physiological Systems
through Biomedical Signals Analysis
PaoloCastiglioni1,*,LucaFaes2andGaetanoValenza3
1 IRCCSFondazioneDonCarloGnocchi,20148Milan,Italy
2 DepartmentofEngineering,UniversityofPalermo,90128Palermo,Italy;[email protected]
3 DepartmentofInformationEngineeringandResearchCenter“E.Piaggio”,UniversityofPisa,56122Pisa,
Italy;[email protected]
* Correspondence:[email protected]
(cid:2)(cid:3)(cid:4)(cid:2)(cid:5)(cid:6)(cid:7)(cid:8)(cid:9)
Received:7September2020;Accepted:8September2020;Published:9September2020 (cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)
Keywords:entropy;multifractality;multiscale;cardiovascularsystem;brain;informationflow
Theideathatmostphysiologicalsystemsarecomplexhasbecomeincreasinglypopularinrecent
decades.Complexityisnowconsideredaubiquitousphenomenoninphysiologyandmedicinethat
allowslivingsystemstoadapttoexternalperturbationspreservinghomeostasis.Complexityoriginates
fromspecificfeaturesofthesystem,likefractalstructures,self-organization,nonlinearity,thepresence
ofmanyinterdependentcomponentsinteractingatdifferenthierarchicallevelsandatdifferenttime
scales,aswellasinterconnectionswithothersystemsthroughphysiologicalnetworks. Biomedical
signalsgeneratedbysuchphysiologicalsystemsmaycarryinformationonthesystem’scomplexity,
whichmaybeexploitedtodetectphysiologicalstates,tomonitorthehealthconditionsovertime,orto
predictpathologicalevents.Forthisreason,themorerecenttrendsintheanalysisofbiomedicalsignals
areaimedatdesigningtoolsforextractinginformationonthesystemcomplexityfromthederivedtime
series,likecontinuouselectroencephalogramandelectromyogramrecordings,beat-by-beatvaluesof
cardiovascularvariables,orbreath-by-breathmeasuresofrespiratoryvariables.
ThisSpecialIssuecollects16scientificcontributionsontherapidlyevolvingfieldoftimeseries
analysisforevaluatingthecomplexdynamicsofphysiologicalsystems.Toprovidethegeneralreaderwith
abroadvisionofthiswideandarticulatedtopic,thisSpecialIssuenotonlycalledfornovelmethodological
approachesdevisedtoimprovetheexistingcomplexityquantifiers,ornovelapplicationsofcomplexity
analysesinphysiologicalorclinicalscenarios,butalsoforreviewpapersdescribingthestateoftheartof
thecomplexitymethodsinspecificareasofclinicalandbiomedicalresearch.
Inthisregard,theSpecialIssueincludestworeviewsaddressingparticularlyrelevantclinical
topics.ThepaperbySunetal.[1]revisesthestudiesonAlzheimer’sdiseasethatquantifiedcomplexity
alterationsinthebrainsignals(electro-andmagneto-encephalographyorfunctionalmagneticresonance
imaging).ThereviewpointsoutalossofsignalcomplexityintheAlzheimerpatientsthatmight
representabiomarkeroftheirfunctionallesions,usefulinthediagnosisofthediseaseandinthe
quantificationofbraindysfunction.ThepaperofRampichinietal.[2]reviewsthestudiesonthe
complexity analysis of the surface electromyography to detect the onset of fatigue in exercising
muscles,anissueofgreatinterestinphysiology,pathophysiology,training,andrehabilitation.Foreach
complexityindex,theauthorssummarizeditsmeaning,theestimationalgorithms,andtheresultsof
thestudiesthatappliedit.
ThenovelmethodologicalapproachesthatthereaderswillfindinthisSpecialIssueregardthe
theoreticalaspectsoftheevaluationofentropyandinformationflow.Thedesiredcharacteristicfor
anyentropyestimatorisrelativeconsistency, inmostapplicationsassumedtomakemeaningful
comparisonsbysettingspecificvaluesoftheestimatorparameters(likeagivenembeddingdimension
Entropy2020,22,1005;doi:10.3390/e22091005 1 www.mdpi.com/journal/entropy