New Methods for Analyzing Complex Biomedical Systems and Signals

144 Pages·2017·13.52 MB·English

Complexity
New Methods for Analyzing
Complex Biomedical Systems and
Signals
Lead Guest Editor: Irini Reljin
Guest Editors: Zoran Obradović, Mirjana Popović, and Valeri Mladenov
New Methods for Analyzing Complex
Biomedical Systems and Signals
Complexity
New Methods for Analyzing Complex
Biomedical Systems and Signals
Lead Guest Editor: Irini Reljin
Guest Editors: Zoran Obradovic, Mirjana Popovic,
and Valeri Mladenov
Copyright©2018Hindawi.Allrightsreserved.
Thisisaspecialissuepublishedin“Complexity.”AllarticlesareopenaccessarticlesdistributedundertheCreativeCommonsAttribu-
tionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Editorial Board
JoséA.Acosta,Spain BernhardC.Geiger,Austria IreneOtero-Muras,Spain
TarekAhmed-Ali,France CarlosGershenson,Mexico YongpingPan,Singapore
RodrigoAldecoa,USA PeterGiesl,UK DanielaPaolotti,Italy
BasilM.Al-Hadithi,Spain SergioGómez,Spain MahardhikaPratama,Singapore
JuanA.Almendral,Spain WesleyN.Gonçalves,Brazil LuisM.Rocha,USA
DiegoR.Amancio,Brazil LingzhongGuo,UK MiguelRomance,Spain
DavidArroyo,Spain XiangguiGuo,China AvimanyuSahoo,USA
MohamedBoutayeb,France SigurdurF.Hafstein,Iceland MatildeSantos,Spain
ArturoBuscarino,Italy ChittaranjanHens,Israel HirokiSayama,USA
GuidoCaldarelli,Italy GiacomoInnocenti,Italy MicheleScarpiniti,Italy
EricCampos-Canton,Mexico SarangapaniJagannathan,USA EnzoPasqualeScilingo,Italy
MohammedChadli,France MahdiJalili,Australia DanSelişteanu,Romania
DiyiChen,China JeffreyH.Johnson,UK DimitriosStamovlasis,Greece
GiulioCimini,Italy M.HassanKhooban,Denmark SamuelStanton,USA
DaniloComminiello,Italy VincentLabatut,France RobertoTonelli,Italy
ManlioDeDomenico,Italy LucasLacasa,UK ShahadatUddin,Australia
PietroDeLellis,Italy XiaopingLiu,Canada GaetanoValenza,Italy
AlbertDiaz-Guilera,Spain RosaM.LopezGutierrez,Mexico DimitriVolchenkov,USA
ThachNgocDinh,France VittorioLoreto,Italy ChristosVolos,Greece
JordiDuch,Spain DidierMaquin,France ZidongWang,UK
MarcioEisencraft,Brazil EulaliaMartínez,Spain Yan-LingWei,Singapore
JoshuaEpstein,USA MarceloMessias,Brazil HongleiXu,Australia
MondherFarza,France AnaMeštrović,Croatia XinggangYan,UK
ThierryFloquet,France Ch.P.Monterola,Philippines MassimilianoZanin,Spain
MattiaFrasca,Italy MarcinMrugalski,Poland HassanZargarzadeh,USA
LuciaValentinaGambuzza,Italy RobertoNatella,Italy RongqingZhang,USA
Contents
NewMethodsforAnalyzingComplexBiomedicalSystemsandSignals
IriniReljin ,ZoranObradović ,MirjanaB.Popović,andValeriMladenov
Editorial(3pages),ArticleID6405121,Volume2018(2018)
FactorAnalysisforFindingInvariantNeuralDescriptorsofHumanEmotions
VitorPereira,FilipeTavares,PetyaMihaylova,ValeriMladenov ,andPetiaGeorgieva
ResearchArticle(8pages),ArticleID6740846,Volume2018(2018)
High-DensityLipoproteins-AssociatedProteinsandSubspeciesRelatedtoArterialStiffnessinYoung
AdultswithType2DiabetesMellitus
XiaotingZhu,AmyS.Shah,DebiK.Swertfeger,HailongLi,ShengRen,JohnT.Melchior,ScottM.Gordon,
W.SeanDavidson,andL.JasonLu
ResearchArticle(14pages),ArticleID7514709,Volume2018(2018)
VectorAutoregressiveHierarchicalHiddenMarkov ModelsforExtractingFingerMovementsUsing
MultichannelSurfaceEMGSignals
NebojšaMalešević ,DimitrijeMarković,GunterKanitz,MarcoControzzi,
ChristianCipriani,andChristianAntfolk
ResearchArticle(12pages),ArticleID9728264,Volume2018(2018)
SparseLearningoftheDiseaseSeverityScoreforHigh-DimensionalData
IvanStojkovicandZoranObradovic
ResearchArticle(11pages),ArticleID7120691,Volume2017(2018)
HSimulator:HybridStochastic/DeterministicSimulationofBiochemicalReactionNetworks
LucaMarchetti,RosarioLombardo,andCorradoPriami
ResearchArticle(12pages),ArticleID1232868,Volume2017(2018)
IdentificationofS1andS2HeartSoundPatternsBasedonFractalTheoryandShapeContext
AnaGavrovska,GoranZajić,VesnaBogdanović,IriniReljin,andBranimirReljin
ResearchArticle(9pages),ArticleID1580414,Volume2017(2018)
InterpolativeBooleanNetworks
VladimirDobrić,PavleMilošević,AleksandarRakićević,
BratislavPetrović,andAnaPoledica
ResearchArticle(15pages),ArticleID2647164,Volume2017(2018)
NewPerspectivesforComputer-AidedDiscriminationofParkinson’sDiseaseandEssentialTremor
P.PovalejBržan,J.A.Gallego,J.P.Romero,V.Glaser,E.Rocon,J.Benito-León,F.Bermejo-Pareja,
I.J.Posada,andA.Holobar
ResearchArticle(17pages),ArticleID4327175,Volume2017(2018)
MonitoringEffectiveConnectivityinthePretermBrain: AGraphApproachtoStudyMaturation
M.Lavanga,O.DeWel,A.Caicedo,K.Jansen,A.Dereymaeker, G.Naulaers,andS.VanHuffel
ResearchArticle(13pages),ArticleID9078541,Volume2017(2018)
Follow-UpandRiskAssessmentinPatientswithMyocardialInfarctionUsingArtificialNeural
Networks
TatjanaGligorijević,ZoranŠevarac,BranislavMilovanović,VladoÐajić,
MarijaZdravković,SašaHinić,MarinaArsić,andMilicaAleksić
ResearchArticle(8pages),ArticleID8953083,Volume2017(2018)
OnConsistencyofCross-ApproximateEntropyinCardiovascularandArtificialEnvironments
TamaraSkoric,OliveraSarenac,BranislavMilovanovic,NinaJapundzic-Zigon,andDraganaBajic
ResearchArticle(15pages),ArticleID8365685,Volume2017(2018)
Hindawi
Complexity
Volume 2018, Article ID 6405121, 3 pages
https://doi.org/10.1155/2018/6405121
Editorial
New Methods for Analyzing Complex Biomedical
Systems and Signals
IriniReljin ,1ZoranObradoviT ,2MirjanaB.PopoviT,1,3andValeriMladenov 4
1SchoolofElectricalEngineering,UniversityofBelgrade,11000Belgrade,Serbia
2CenterforDataAnalyticsandBiomedicalInformatics,TempleUniversity,Philadelphia,PA19122,USA
3InstituteforMedicalResearch,UniversityofBelgrade,11000Belgrade,Serbia
4DepartmentofTheoreticalElectricalEngineering,TechnicalUniversityofSofia,1000Sofia,Bulgaria
CorrespondenceshouldbeaddressedtoIriniReljin;[email protected]
Received 13 March 2018; Accepted 13 March 2018; Published 24 May 2018
Copyright©2018IriniReljinetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
In recent years, there has been significant progress in the authors found, among 68 detected phospholipid-associated
applications of sensing devices in acquiring biomedical proteins,sevenasnegativelycorrelatedwithPWV,indicating
signals. Availability of big and/or high dimensional data theymaybeatheroprotective,andnineproteinsaspositively
obtained by new instruments inspired the development of correlated with PWV, suggesting they may be related to
novel information processing methods aimed at signifi- arterialstiffness.
cantly improving the understanding of subtle and com- Pathologicaltremorisacomplexmovementdisorderof
plex mechanisms of biomedical processes. Such data have differentcauses,affectingabout5%ofthepopulationoverthe
provedusefulindiagnosis,predictionofhealthstate,survival ageof65.ThequantificationanddiscriminationofParkinso-
prognosis, and therapy optimization. New sensing devices niandisease(PD)andessentialtremor(ET)havesignificant
generate precise signals, large-scale biomedical data which impact on both patient treatment and the development of
hasopenedchallengingproblemsformanaging,processing, newinterventionsinclinicalpractice.Inthepaper“NewPer-
storing,retrieving,transferring,andinterpretingsuchdata. spectivesforComputer-AidedDiscriminationofParkinson’s
Therefore, we have invited the researches to contribute Disease and Essential Tremor,” P. P. Brzˇan et al. proposed
their research on new algorithms and tools for modeling, novelmetricstoquantifyanddiscriminatePDandETtremor.
processing,andanalyzingcomplexbiomedicalsignals.After Thesemetrics,aswellasotherknowntremor-relatedmove-
rigorousreview,thisspecialissueacceptedelevenarticleson mentmetrics,weretestedover27PDand27ETpatients.The
thistopicwhicharesummarizedhere. discriminatory power of all of these metrics was evaluated
Inthepaper“High-DensityLipoproteins-AssociatedPro- basedonthereceiveroperatingcharacteristics,decisiontree
teins and Subspecies Related to Arterial Stiffness in Young modeling, and binary logistic regression. After testing the
AdultswithType2DiabetesMellitus,”X.Zhuetal.analyzed discriminativepowerofindividualmetrics,itwasfoundthat
the correlation of phospholipid-associated protein with a thenewmetricsoutperformedalternativemethods.
pulse wave velocity (PWV) in order to find which protein Auscultation is widely used for evaluation of cardiac
isrelatedtoarterialstiffnessinpersonswithtype2diabetes function. Such analysis mostly depends on the individual
mellitus (T2D). Five adolescents with T2D and six lean skills of the physician, and therefore there is a growing
controls with no evidence of chronic disease were under demand for automatic heart sound interpretation methods.
consideration.Byintegratingpatientclinicaldata,biological Theinitialstepinheartsoundanalysisistoidentifyprimary
experiments, and bioinformatics analysis to reveal arterial heartsounds,S1andS2.Properidentificationofthesesounds
stiffness-relatedhigh-densitylipoproteinsandsubspecies,the is of key importance for identifying other components of
2 Complexity
heartsignal,suchasextrasoundsandmurmurs.Inthepaper In the paper “Sparse Learning of the Disease Severity
“Identification of S1 and S2 Heart Sound Patterns Based Score for High-Dimensional Data,” I. Stojkovic and Z.
on Fractal Theory and Shape Context,” A. Gavrovska et al. Obradovic proposed a novel approach to the problem of
proposed a new method for identifying and differentiating learning disease severity scores in presence of irrelevant or
primary heart sounds S1 and S2, without the electrocardio- high-dimensionalmeasurements.Theproposedformulation
gramreference.Thenewmethodisbasedonadaptivefiltering ofsparseseverityscorelearningforcesweightsofmostofthe
andamodifiedblanketapproach,whichdescribetheshapeof features to be exactly zero, therefore effectively performing
the heart signal. After determining proposed characteristic featureselectionbylearningthesparselinearscoringfunc-
descriptors, the SVM-based classification and the fivefold tion. This novel severity score objective function is convex
cross-validationhavebeenperformed.Ninehundredsound andnonsmoothanditprecludesthedirectuseofconvenient
sequencescollectedfromtheUniversityChildren’sHospital optimizationtoolslikegradient-basedmethods.Theydefined
in Belgrade, Serbia, were analyzed. The evaluation results the severity score as a linear combination of intensities of
are obtained by using the receiver operating characteristic themostrelevantfeatures.Theproposedapproachmightbe
(ROC) curve. Calculated AUC (area under the curve) and used as an effective and reliable tool for both scoring func-
the classification accuracy and 𝐹-measure were used as tion learning and biomarker discovery, as demonstrated by
performance measures. Results are compared with other identifyingastablesetofgenesrelatedtoinfluenzasymptoms’
knownshaperelatedmethods.Theproposedmethod,using severity,whichareenrichedinimmune-relatedprocesses.
onlythreedescriptors,isofhighaccuracy(higherthan95%) Modeling and representing biological processes are a
andoutperformsothermethodsbyatleast6%. challenging problem for biochemical researchers. Compu-
Inthepaper“Follow-UpandRiskAssessmentinPatients tational systems biology is becoming a fundamental tool of
with Myocardial Infarction Using Artificial Neural Net- life-scienceresearch,whichaimsatdevelopingmodelsrep-
works,” T. Gligorijevic´ et al. described a novel method for resenting biological phenomena and reliable computational
identifying individuals with high risk of death after acute techniques for their simulation. In the paper “HSimulator:
myocardial infarction by using artificial neural networks Hybrid Stochastic/Deterministic Simulation of Biochemical
(ANNs).TrainingdataforANNwas1,705patientsadmitted ReactionNetworks,”L.Marchettietal.consideredthisprob-
between 2003 and 2013 to Coronary Care Unit of Clinical lem and described the simulator for mass-action reaction.
Hospital Center Bezˇanijska Kosa, Belgrade, Serbia, who They combined several strategies including exact stochastic
underwent 24-hour ECG monitoring, short ECG analysis, simulation,deterministicsimulation,andhybridsimulation.
noninvasivebeat-to-beatheartratevariability,andbaroreflex For deterministic and hybrid simulations, the simulator
sensitivity. The proposed neural network classifier showed automatically translates the mass-action reaction network
goodperformanceforsurvivalprediction:88%accuracy,81% intoasetofordinarydifferentialequationssuitableforfurther
sensitivity, 93% specificity, 0.85 𝐹-measure, and area under resolving.Theirsimulatorprovidesasuiteofstate-of-the-art
thecurvevalueof0.77.Thesefindingssupportthetheorythat simulation algorithms including the exact algorithm, RSSA
patients with high sympathetic activity (reduced baroreflex (rejection-based stochastic simulation algorithm), and the
sensitivity)haveanincreasedriskofmortalityindependent firstpubliclyavailableimplementationofitshybridversion,
of other risk factors and that artificial neural networks can HRSSA. The benchmarks in the paper show that their
indicatehigherriskindividuals. simulator implementation is often faster than the state-of-
The approximate entropy (ApEn) is one of the most the-artCOPASI(complexpathwaysimulator)simulator.
used nonlinear techniques for quantifying the complexity Inthepaper“InterpolativeBooleanNetworks,”V.Dobric´
and unpredictability of time series. The ApEn is approved etal.proposedageneralizedBooleannetworkadaptivewith
as a supporting tool in preclinical and clinical studies, with respect to the nature of input variables which offers greater
the most prominent applications in cardiovascular studies. descriptive power as compared with traditional models.
One of the very first ApEn modifications is the Cross- Instead of classical Boolean networks, which are binary, by
ApEn (XApEn) that estimates mutual predictability of two introducing the gradation in this model more descriptive
simultaneously recorded time series. In view of ApEn as a power can be provided. This approach, called by authors
complexity measure of solitary signal, XApEn should have theinterpolativeBooleannetworks,isbasedoninterpolative
becomeanequivalenttoolforpairedphysiologicalprocesses. Boolean algebra: the [0,1]-valued realization of Boolean
Inthepaper“OnConsistencyofCross-ApproximateEntropy algebra. Interpolative Boolean networks can be used to
in Cardiovascular and Artificial Environments,” T. Skoric predictbehaviorofcomplexsystems.Forthepurposeoftheir
et al. studied the problems that preclude wider XApEn practical application, a simple software tool is developed.
implementationandproposedmethodsfortheiradjustment. When applied on two examples from the literature and
Their study used three types of data: artificial (for testing compared against other real-valued logical approaches, the
thestabilityofestimatedvalues),animal(formethoddevel- proposedinterpolativeBooleannetworksprovidedasuperior
opment and for repeated testing), and human (to validate descriptivepower.
the derived methods in typical real applications where the In the paper “Monitoring Effective Connectivity in the
records are short). The new approach is verified by using PretermBrain:AGraphApproachtoStudyMaturation,”M.
cardiovascularsignalsrecordedfromrats(longsignals)and Lavanga et al. investigated effective electroencephalography
healthyvolunteers(shortclinicalsignals),proposingachange (EEG)basedbrainconnectivityinprematureinfants,whose
oftraditionalparameterguidelines. postmenstrual age ranged from 27 to 42 weeks. Results
Complexity 3
showed that the EEG-graphs changed with age in terms of submitted papers and providing helpful comments. Finally,
topology. In particular, the clustering coefficient and the we thank the journal’s Editorial Board for their help and
spectral radius decreased with maturation, while the path assistance.
lengthincreased.ResultsshowedthattheEEG-connectivity,
IriniReljin
assessed using graph theory indices, moved from a small-
ZoranObradovi´c
worldnetworktoarandomone,sincetheclusteringcoeffi-
MirjanaB.Popovi´c
cient increases and the path length decreases. Their results
ValeriMladenov
showedthatitispossibletopredicttheageoftheinfantwith
arootmean-squarederrorequalto2.11weeks.Theseresults
areconsistentwiththeonesreportedintheliteratureforage
predictioninpretermbabies.
Overthelastdecade,variousmethodswereproposedfor
characterizinghumanemotionsfromelectroencephalogram
(EEG). In the paper “Factor Analysis for Finding Invariant
Neural Descriptors of Human Emotions,” V. Pereira et
al. proposed a new spatial-temporal feature set with clear
physicalgroundoverselectedEEGelectrodesandthefactor
analysis (FA) as an alternative method to extract the most
important components of the feature set. In contrast to the
widelyusedprincipalcomponentanalysis,FApreservesthe
relationbetweenthefactorsandthefeaturesetandtherefore
theconclusionsaremoreeasilyinterpretedbypsychologists
and neurophysiologists. The combination of the proposed
features and FA resulted in finding the most representative
neural descriptors of the emotion valence, invariant with
respecttotrialsandsubjects.Theauthorsprovidedevidence
thatthefactoranalysisofevent-relatedpotentialsisapromis-
ing approach to extract statistical underlying correlations
of the brain activity among subjects and therefore decode
humanemotionalstates.Byextractingtemporalandspatial
EEG features, they showed that it is possible to build Brain
Computer Interface for decoding human emotions across
varioussubjects.
Inthepaper“VectorAutoregressiveHierarchicalHidden
MarkovModelsforExtractingFingerMovementsUsingMul-
tichannelSurfaceEMGSignals,”N.Maleˇsevic´etal.presented
anovelcomputationaltechniqueintendedfortherobustand
adaptablecontrolofamultifunctionalprosthetichandusing
multichannelsurfaceelectromyography(EMG).Theirstudy
indicates that using mean absolute value feature coupled
with hierarchical hidden semi-Markov models algorithm
leads to movement decoding accuracy higher than other
combinations of features and classifiers. This combination
also guaranties the shortest motion selection and motion
completiontimes,influencingaresponseofaprosthetichand
to a user intent. Their algorithm is characterized by low
computationalcomplexityfortheexecution,easyexpansion,
andnoiseresiliency.
We believe this special issue is useful for investigators
who work with complex biomedical signals. We hope that
the papers from this issue will initiate further interest in
analyzingcomplexbiomedicalsystems.
Acknowledgments
The guest editorial team thanks the authors who submitted
their papers to this special issue. Also, our sincere thanks
go to reviewers for their dedicated efforts in evaluating the