ECG-based heartbeat classification for arrhythmia detection : a survey.

(1)

jou rn al h om ep a ge :w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b

ECG-based

heartbeat

classiﬁcation

for

arrhythmia

detection:

A

survey

Eduardo

José

da

S. Luz

a

_,

_William

_Robson

_Schwartz

b

_,

Guillermo

Cámara-Chávez

a

_,

_David

_Menotti

a,c,∗

a_Universidade_Federal_de_Ouro_Preto,_Computing_Department,_Ouro_Preto,_MG,_Brazil

b_Universidade_Federal_de_Minas_Gerais,_Computer_Science_Department,_Belo_Horizonte,_MG,_Brazil c_Universidade_Federal_do_Paraná,_Department_of_Informatics,_Curitiba,_PR,_Brazil

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received27March2015 Receivedinrevisedform 8November2015

Accepted17December2015

Keywords:

ECG-basedsignalprocessing Heartbeatclassiﬁcation Preprocessing Heartbeatsegmentation Featureextraction Learningalgorithms

a

b

s

t

r

a

c

t

Anelectrocardiogram(ECG)measurestheelectricactivityoftheheartandhasbeenwidely usedfordetectingheartdiseasesduetoitssimplicityandnon-invasivenature.Byanalyzing theelectricalsignalofeachheartbeat,i.e.,thecombinationofactionimpulsewaveforms producedbydifferentspecializedcardiactissuesfoundintheheart,itispossibletodetect someofitsabnormalities.Inthelastdecades,severalworksweredevelopedtoproduce automaticECG-basedheartbeatclassificationmethods.Inthiswork,wesurveythecurrent state-of-the-artmethodsofECG-basedautomatedabnormalitiesheartbeatclassification bypresentingtheECGsignalpreprocessing,theheartbeatsegmentationtechniques,the featuredescriptionmethodsandthelearningalgorithmsused.Inaddition,wedescribe someofthedatabasesusedforevaluationofmethodsindicatedbyawell-knownstandard developedbytheAssociationfortheAdvancementofMedicalInstrumentation(AAMI)and describedinANSI/AAMIEC57:1998/(R)2008(ANSI/AAMI,2008).Finally,wediscusslimitations anddrawbacksofthemethodsintheliteraturepresentingconcludingremarksandfuture challenges,andalsoweproposeanevaluationprocessworkflowtoguideauthorsinfuture works.

1. Introduction

Therearevarioustypesofarrhythmiasandeachtypeis associ-atedwithapattern,andassuch,itispossibletoidentifyand classifyitstype.Thearrhythmiascanbeclassiﬁedinto two majorcategories.Theﬁrstcategoryconsistsofarrhythmias formedbyasingleirregularheartbeat,hereincalled morpho-logicalarrhythmia.Theothercategoryconsistsofarrhythmias

∗ _{Corresponding}_author_at:_Universidade_Federal_do_Paraná,_Department_of_Informatics,_81.531-980_Curitiba,_PR,_Brazil. Tel.:+554133613206;fax:+554133613031.

E-mailaddresses:eduluz@iceb.ufop.br(E.J.d.S.Luz),william@dcc.ufmg.br(W.R.Schwartz),guillermo@iceb.ufop.br(G.Cámara-Chávez),

menotti@iceb.ufop.br,menotti@inf.ufpr.br(D.Menotti).

formedbyasetofirregularheartbeats,hereincalledrhythmic arrhythmias.Theclassiﬁcationofnormalheartbeatsandthe onescomposingtheformergroupareonthefocusofthis sur-vey.Theseheartbeatsproducealterationsinthemorphology orwavefrequency,andallofthesealterationscanbeidentiﬁed bytheECGexam.

Theprocessofidentifyingandclassifyingarrhythmiascan beverytroublesomeforahumanbeingbecausesometimes itisnecessarytoanalyzeeachheartbeatoftheECGrecords,

http://dx.doi.org/10.1016/j.cmpb.2015.12.008

(2)

acquiredbyaholtermonitorforinstance,duringhours,or evendays.Inaddition,thereisthepossibilityofhumanerror duringtheECGrecordsanalysis,duetofatigue.Analternative istousecomputationaltechniquesforautomatic classiﬁca-tion.

Afullautomaticsystemforarrhythmiaclassificationfrom signals acquired by a ECG device can be divided in four steps(seeFig.1),asfollows:(1)ECGsignalpreprocessing;(2) heartbeatsegmentation;(3)featureextraction;and(4) learn-ing/classification.Ineachofthefoursteps,anactionistaken andthefinalobjectiveisthediscrimination/identificationof thetypeofheartbeat.

Thefirst twostepsofa suchclassificationsystem (ECG signalpreprocessingandheartbeatsegmentation)havebeen widely explored in the literature [2–6]. The techniques employedduringthepreprocessingstepdirectlyinfluencethe finalresults,andtherefore,shouldbecarefullychosen.The resultsrelatedtotheheartbeatsegmentationstep,inthecase ofQRSdetection, are veryclosetooptimal.However,there isstillroomforexplorationandimprovementsinthesteps relatedtoclassification(featureextractionandlearning algo-rithms).EventhoughtheproblemofECGdelineationisstill open,itisnotsousefulforthemethodsintheliterature sur-veyedhere.

Thispaperpresentsasurveyofexistingstudiesfoundin literatureregardingtheECG-basedarrhythmiaclassiﬁcation methodsanddiscussesthemaintechniquesusedforthe con-struction ofthese automaticsystems aswell astwo main paradigmsusedforevaluation:inter-patientandintra-patient

[7,8].Inaddition,themostpopulardatabasesandthe

prob-lemsrelatedtotheevaluationofcurrentmethodsfoundin literaturearealsodiscussed.Fromthisdiscussion,aworkflow isproposedtoguidetheevaluationprocessoffutureworks. Notethatthisworkflowforevaluationprocessconstitutesan importantcontributionofthissurveywork.Intheliterature, wefindasurveyofknowledge-basedECGinterpretation[9]

reviewingmethodsproposedinthe20thcentury.Cliffordetal.

[1]performedanextensivesurveyonthemethodsusedfor ECGsignalanalysis.Their study focusedon thephysiology ofthesignal,aswellasitsprocessingtechniques,mainlyon thefeatureextractionandclassiﬁcation.Inparticular,Clifford etal.[1]didnotfocusontheproblemofevaluatingmethods, whichisthedifferentialofourstudy,inadditiontoamore up-to-dateliteraturereviewontheissue.Moreover,oursurveyon featureextractionbringsaspecialreviewonfeatureselection. Theremainingofthispaperisorganizedasfollows. Sec-tion 2introducesthe fundamental aspects ofECGsignals; thestate-of-artisdescribed inSections 3,4, 5, and 6; and theevaluationstandardsdevelopedbytheAssociationforthe AdvancementofMedicalInstrumentation(AAMI)[10]andthe databasesrecommendedforthesestandards,together with thecriticismsrelatedtothesystemsdevelopeduptodateand futurechallenges,arediscussedinSections7,8,and9.

More speciﬁcally, Section 3 deals with the preprocess-ing techniquesmost utilized inECG signals, whileSection

4presents the concept ofsegmenting heartbeats from the ECG signals and its commonly employed techniques. Sec-tion 5dealswiththekeypointforthesuccessofarrhythmia classiﬁcation,i.e.,therepresentationofaheartbeatorthe fea-tureextractionprocess.Section 6discussesthemostpopular

learningalgorithmsfoundinliteratureforarrhythmia clas-siﬁcation. Section 7presents the recommendedevaluation standard proposed byAAMI and describesthe characteris-ticsofthemostutilizeddatabases,indicatedbythestandard, toevaluatetheclassiﬁcationarrhythmiamethods.Section 8

presentssomecommentsrelatedtotheissueofselectingdata forlearning/evaluatingmodelsforarrhythmiaclassification anditsimpactonthefinalresult.Finally,Section 9discusses thelimitationsandproblemsofthefieldandpointoutfuture challengesfortheresearchcommunity.

2. ECG

signal

Theheartisamusclethatcontractsinarhythmicalmanner, pumpingbloodthroughoutthebody.Thiscontractionhasits beginningattheatrialsinenodethatactsasanatural pace-maker,andpropagatesthroughtherestofthemuscle.This electricalsignalpropagationfollowsapattern[11].Asaresult ofthisactivity,electricalcurrentsaregeneratedonthesurface ofthebody,provokingvariationsintheelectricalpotentialof theskinsurface.Thesesignalscanbecapturedormeasured withtheaidofelectrodesandappropriateequipment.

Thedifferenceofelectricalpotentialbetweenthepoints marked bytheelectrodes onthe skin,usuallyisenhanced withtheaidofaninstrumentation(operational)amplifierwith opticisolation.Then,thesignalissubmittedtoahigh-pass filter; and as a second stage, submitted to an antialiasing low-pass filter. Finally, it appears in ananalogical to digi-tal converter. Thegraphical registration ofthis acquisition process iscalledelectrocardiogram (ECG) (seeFig. 2).Since AugustusDesiréWallerdemonstratedthefirsthumanECGin 1887,theelectricalactivityofthehearthasbeenrecorded[12]. Evenso,theabilitytorecognizethenormalcardiacrhythm and/orarrhythmiasdidnotbecomeroutineinmedical check-upsuntil1960.

Nowadays, there are many approaches to measure-ment/recordECG.daSilvaetal.[13]providedataxonomyof state-of-the-artECGmeasurementmethods:in-the-person, on-the-personandoff-the-person.

Withinthein-the-personcategory,thereare equipments designedtobeusedinsidehumanbody,suchas surgically implantedones,subdermalapplicationsoreveningestedin theformofpills.Thesedevicesareusedwhenlessinvasive approacharenotapplicable.

Contrastingwiththein-the-personcategory,thereis off-the-person category. Deviceson this categoryare designed tomeasureECGwithoutskincontactorwithminimalskin contact.Accordingto[13],thiscategoryisalignedwithfuture trendsofmedicalapplicationwherepervasivecomputer sys-temsareareality.Examplesofsuchequipmentsaretheones basedoncapacitivedeviceswhichmeasuretheelectricﬁeld changesinducedbythebodyallowingECGmeasurementat distanceof1cmormoreevenwithclothingbetweenthebody andthesensor[13–15].

ThemajorityofdevicesusedforECGmeasurementsarein theon-the-personcategory.Devicesonthiscategorynormally requiretheuseofsomeelectrodesattachedtotheskin sur-face.Examplesofsuchequipmentsarebedsidemonitorsand

(3)

Fig.1–Adiagramofthearrhythmiaclassiﬁcationsystem.

High-Pass Band Filter

Isolation Low-Pass Band Filter

Analog to Digital Converter (ADC) source _ECG signal Amplification

Fig.2–SimpliﬁeddisplayofthehardwareforthecaptureofECGsignals.Source:AdaptedfromRef.[1].

holters.Nowadays,thestandarddevices usedforheartbeat analysiscomefromthiscategory.

Onequipmentsbelongingintotheon-the-personcategory, threeormoreelectrodesareusedtoobtainthesignal,inwhich oneofthemservesasareferencefortheothers.Usually,the referenceelectrodeisplacedneartherightleg.Assuch,there canbedifferentvisionsoftheECGsignal,dependingonthe pairofelectrodeschosentoconstructthesignal.These differ-entiatedvisionsaregiventhenameofleads.

Awidelyusedconﬁgurationofelectrodesisonecomposed of5electrodes[16]:oneoftheelectrodesispositionedonthe leftarm(LA),oneontherightarm(RA),oneontheleftleg(LL), oneontherightleg(RL)andoneonthechest,totherightof theexternal(VorV1).Anotherwidelyemployedsetupuses 10electrodes[16],where5extraelectrodes(besidesVorV1 onthe chestand LA,RD,LL andRAon legsandarms)are positionedonthechest(V2toV6)allowingaformationof12 leads.The10electrodes(12leads)conﬁgurationcanbeseen inFig.3.

Fromtheseconﬁgurations,severaldifferentleadscanbe constructedtovisualizethe ECGsignal. Forexample,Fig.4

illustrates3particularleads:(I)formedbytheelectrical poten-tialdifferencebetweentheLAandRAelectrodes;(II)formedby theelectricalpotentialdifferencebetweentheLLandRA elec-trodes;and(III)formedbytheelectricalpotentialdifference betweentheLLandLAelectrodes.

ThepreviouslydescribedleadIIisoneofthemostutilized fordiagnosingheartdiseases.Ithighlightsvarioussegments withinthe heartbeat, besidesdisplaying three ofthe most important waves: P, QRS and T (see Fig. 5). These waves correspondtotheﬁeldinducedbytheelectricalphenomena occurring on the heart surface, denominated atrial depo-larization (P wave), ventral depolarization (QRS complex

Fig.3–Typical10electrodesconﬁguration.

wave) and repolarization (T wave). The patterns provoked byarrhythmiascandeeplychangethesewaves.Meanwhile, leadVanditscorrelateleads(V1,V2)favortheclassiﬁcation ofventricularrelatedarrhythmias,sincethereareelectrodes

(4)

Tasc. Tdesc. Q P ST Segment QT Interval S PR Segment PR Interval R QRS Complex TPeak TEnd U TpeInterval

Fig.5–Fiducialpointsandvarioususualintervals(waves) ofaheartbeat.

Source:Ref.[21].

positioned on the chest, improving the registry of action potentialsonventricularmuscle.

Therefore,theleadsmostutilizedfortheautomatic heart-beat and arrhythmia classiﬁcation are leads II and V and themethodsthatuseacombinationofthesetwoleads(and othercombinations)aretheonesthatpresentthebestresults todate[17]. Inthissense, therecentwork byTomasicand Trobec[18]reviewsmethodsworkingwithreducednumbers ofleadsandapproachesforthesynthesisofleads, conclud-ing thatthe traditional12 lead systemcan besynthesized from asmaller number of measurements[19]. In contrast, anotherstudypublishedbydeChazal[20]demonstratedthat similareffectivenessforECGarrhythmiaclassiﬁcationcanbe obtainedatalessercomputationalcostwhenusingonlyone lead,comparedwithmethodsusingmultipleleads[7].

Although on-the-person is the mainstream on devices aimingheartdiseasesdiagnoses,[13]haveshownthatdata captured with off-the-person based devices can be highly correlated to data captured with traditional on-the-person based equipments. The authors claim that off-the-person basedequipmentscanextendpreventivemedicinepractices byallowingECGmonitoringwithoutinterferenceondaily rou-tine.In thatsense, we encourageresearcherstobuild ECG databases basedon off-the-person devices toevaluateand validateheartbeatclassiﬁcationmethodsforthatcategory.

3. Preprocessing

Amongall proposalsforreducingnoise inECGsignals,the simplestandmostwidelyusedistheimplementationof recur-sive digital ﬁltersof the ﬁniteimpulse response(FIR) [22], whichwasmadecomputationallypossiblewiththeadvance in microcontrollers and microprocessors. These methods workwellfortheattenuationoftheknownfrequencybands, suchasthenoisecomingfromtheelectricalnetwork(50Hz

or60Hz),sincetheyallowquickandeasyapplicationofthe reject-band-filter.Theproblemwiththisapproachisthatthe frequencyofthe noise isnotalwaysknown,which canbe solvedbyapplyingfiltersforvariousfrequencybandstothe signal. However,the indiscriminateuse offilters, i.e., high-passandlow-passones,distortsthemorphologyofthesignal, and many times, makesitunusable fordiagnosing cardiac diseases.Architectureswithadaptivefilters[23,24]werealso employedfornoiseremovalfromtheECGsignals.However, according toThakor and Zhu [25], this techniquehas con-straintsanddoesnotoffergreatadvantagesovertheFIRdigital filters.Xueetal.[26]surmountsomeofthesedifficultiesby using adaptive filters based on neural networks such that thenoisereductionwassignificantlyimproved.Thisstrategy proportionedbetterdetectionoftheQRScomplex,when com-paredwiththesamemethodusinglinearlyadaptivefilters.

Inthelastdecade,manymethodsbasedonwavelet trans-forms have been employed to remove noise, since they preserveECGsignalpropertiesavoidinglossofitsimportant physiological details and are simple from a computational point ofview[27–29]. Sayadi and Shamsollahi[2] proposed a modiﬁcation of the wavelet transform called the multi-adaptivebionicwavelettransformanditwasappliedtoreduce noiseandbaselinevariationoftheECGsignal.Thismethod presentedsuperiorresultswhencomparedtotheonesbased onthetraditionalwavelettransform.

Othermethodshavealsopresentedinterestingresultson noise attenuation.Samenietal.[30]haveproposed theuse ofnonlinearBayesianﬁltersforECGsignalnoisereduction, presentingpromisingresults.Anewalgorithmbasedonthe ExtendedKalmanFilter[3],whichincorporatesthe parame-ters ofthe ECGdynamic modelforECG noise reductionand signalcompression,yieldedasigniﬁcantcontributionbecause themethodshowedthegreatesteffectivenesstodate.Note thattheworksin[2,3,30]reporttheirresultsintermsofsignal tonoiseratio.

Techniques for preprocessingthe ECGsignal are widely explored,butthechoiceofwhichmethodtouseisintrinsically connectedwiththefinalobjective oftheresearch.Methods focusing onthe heartbeat segmentation from theECG sig-nal(i.e.,detectionoftheQRScomplex,otherwavesorfiducial pointsaimingatheartbeatdelimitation)tendtorequirea pre-processingthatisdifferentfromthemethodsfocusingonthe automaticclassificationofarrhythmias.

Table7sumarizesthemainreviewedreferencesof

meth-odsaimingatheartbeatclassificationandthistableisexplored further(Section 8).ThosemethodsfollowAAMIinstructions andthesameprotocoltoreporttheresults,butdifferent pre-processingtechniquesareused.deChazaletal.[7]usedtwo medianfilterstoremovebaselinewander.Onemedianfilter of200-mswidthtoremoveQRScomplexesandP-wavesand otherof600mswidthtoremoveT-waves.Theresultingsignal isthenfilteredagainwitha12-tap,low-passFIRfilterwith 3-dBpointat35Hz.Samepreprocessingisusedin[31–35,8,36]. In[37]signalispreprocessedwith10thorderlowpassFIR fil-ter.Ye etal.[38]usedawavelet-based approachtoremove baselinewander[39]andthenaband-passfilterat0.5–12Hz isappliedtomaximizeQRScomplexenergy.Bazietal.[40] pro-posedtheuseofhighpassfilterfornoiseartifactsandanotch filterforpowernetworknoise.LinandYang[41]usesasecond

(5)

orderlowpassﬁlterandtwomedianﬁlter.In[42],thesignalis subtractedbyitsmeanandthennormalized.Escalona-Moran etal.[43]usedtherawwavei.e.,nopreprocessingisapplied.

NotethatthemethodscitedinTable7usedifferent prepro-cessingapproaches.However,theimpactoftheseapproaches onautomaticarrhythmiaclassificationmethodsisnotclear. Theconsidered state-of-the-artmethodsdonotevenapply preprocessing on the signal. Although some studies exist relatingpreprocessingtechniqueswiththefinalperformance ofthe automaticclassificationofarrhythmias, suchasthe workpresentedin[44],theyareinsufficientinnumberand moreresearchinthisareaisencouraged.Itisworthnoting thatthestate-of-the-artmethodsforautomaticarrhythmia classificationdonotusestate-of-the-artpreprocessing meth-odssignaltonoiseratioimprovement.

4. Segmentation

Heartbeatsegmentationmethods(i.e.,detectionoftheRpeak ortheQRScomplex)havebeenstudiedformorethanthree decades [49,53,46,54,55] and the generations of these algo-rithmsand newlydevelopingmethodsreﬂecttheevolution ofthe processingpower of computers.With the facility of usingfasterprocessingcomputers,authorsstoppedworrying aboutcomputationalcostand startedconcentratingonthe heartbeatsegmentationaccuracy.Twomeasuresareusually consideredforevaluatingtheaccuracyofheartbeat segmen-tation:sensitivityandpositivepredictivity,whicharedeﬁned as:

Sensitivity_SEG=TP/(TP+FN), (1)

and

Positivepredictivity_SEG=TP/(TP+FP), (2) whereTP(TruePositive),FP(FalsePositive)andFN(False Neg-ative)indicatethenumberofheartbeatscorrectlysegmented, numberofsegmentationsthatdonotcorrespondtothe heart-beats,andnumberofsegmentationsthatwerenotperformed, respectively.

Forafaircomparisonofthemethodsfocusingonthe heart-beatsegmentation,astandarddatabaseneedstobeused.The mostutilized,andrecommendedbyANSI/AAMIforthe vali-dationofmedicalequipment[10],istheMIT-BIHdatabasefor arrhythmiaanalysis[56]–inthiscase,usedforheartbeat seg-mentation,althoughotherdatabasesarealsoused,suchas thatofAHA[57]andthatofCSE[58].However,accordingto Kohleretal.[59],manyofthemethodspresentedinthe liter-aturedonotuseastandardizeddatabase,oruseonlypartof it,whichmakesitdifﬁculttofairlycomparemethods.

Anapproachwidelyusedforsegmentation,duetoits sim-plicityand promisingresults, isbased on digital filtersfor the attenuation of the noise and removal ofthe fluctuat-ingbaseline,nonlineartranslationsthatenhancetheRpeak andadaptivedetectionthresholdwereproposedbyPanand Tompkins[49].Moresophisticatedmethodshavealsobeen used,suchasmethodsbasedonneuralnetworks[53],genetic algorithms[50],wavelettransform[60,61,4],filterbanks[46],

Quad Level Vector [62], among others. Table 1 displays the performance ofsomemethods forheartbeat segmentation thatusetheMIT-BIHdatabaseforevaluation.Notethatthe SensitivitySEG (Se)andPositivepredictivitySEG (+P) valuesdo notshowgreatdifferencesinthemethodsstudied.Itis impor-tant tohighlight that the methodspresentedinthis table, contemplatedalargespectrumofcomplexity,i.e.,fromvery simplemethodstomoreelaboratedones.

Somealgorithmsalsoproposetoidentifyotherwaves asso-ciatedwithheartbeats,suchasthePwaveandtheTwave

[4,63–65], which canbeuseful forarrhythmiaclassiﬁcation

methods, sincemoreinformationabouttheheartbeats can beobtained.

Althoughheartbeatsegmentationisnotthemainfocusof thissurvey,notethatthisstageisofparamountimportance inthearrhythmiaheartbeatclassificationprocess,sincesome errorsherearepropagatedtothefollowingstagesandhave astrongimpactinthefinalclassificationofthearrhythmia system.However,alargemajorityofthereviewedresearches hereinutilizeddatabasesinwhichtheeventsrelatedto heart-beatsegmentation,i.e.,thedetectionoftheRpeakortheQRS complex,areidentifiedandpreviouslylabeled,reducingthe segmentationstagetoasimplesearchofalabeledeventin thedatabase.Inthisway,theresultsreportedbytheseworks disregardtheimpactofsegmentationstepeventhoughthe databaselabelingispronetohumanerrors.Therefore, eval-uating theimpact ofdifferentsegmentation algorithmson automaticarrhythmiaclassificationmethodscanbea promis-ingresearchdirection.

Yeetal.[66]proposedatesttoinvestigatetherobustness oftheirfeatureextractionmethodagainstonesegmentation issue,theR-peakmislocateerror.AGaussian-distributed arti-ﬁcialjitterwasusedtoadderroronR-peakannotations.We suggest toother authors toincorporatesuchtest infuture worksaimingautomaticheartbeatclassiﬁcation.

5. Feature

extraction

Thefeatureextractionstageisthekeytothesuccessinthe heartbeatclassiﬁcationofthearrhythmiausingtheECG sig-nal. Any informationextractedfrom the heartbeat usedto discriminateitstypemaybeconsideredasafeature.The fea-turescanbeextractedinvariousformsdirectlyfromtheECG signal’s morphology inthe time domainand/or inthe fre-quency domain or from the cardiac rhythm. Mostpopular methodsproposedinliteraturearediscussedinSection5.1.

Eventhoughsomeworksregardfeatureextractionand fea-tureselectionastwointerchangeableterms,thesetwoprocess are infact different. Whilefeature extractionisdeﬁnedas the stagethat involves the descriptionof aheartbeat, fea-ture selectionconsistsinchoosinga subsetwith themost representativefeatureswiththeobjectivetoimprovethe clas-siﬁcationstage.Section 5.2isdedicated todescribefeature selectionapproaches.

5.1. Featureextraction

Themostcommonfeaturefoundintheliteratureiscalculated fromthecardiacrhythm(orheartbeatinterval),alsoknownas

(6)

Table1–Effectivenessofheartbeatsegmentationmethods.#and%standforabsoluteandpercentagenumbers.The

MIT-BIHArr.databaseisusedinallmethods.

Method Heartbeats TP FP FN Error Se +P

(#) (#) (#) (#) (%) (%) (%)

Martinezetal.[4] 109428 109208 153 220 0.34 99.80 99.86

MoodyandMark[45] 109428 107567 94 1861 1.79 98.30 99.91

Lietal.[5] 104182 104070 65 112 0.17 99.89 99.94

Afonsoetal.[46] 90909 90535 406 374 0.86 99.59 99.56

Bahouraetal.[6] 109809 109635 135 184 0.29 99.83 99.88

Leeetal.[47] 109481 109146 137 335 0.43 99.69 99.88

HamiltonandTompkins[48] 109267 108927 248 340 0.54 99.69 99.77

PanandTompikins[49] 109809 109532 507 227 0.71 99.75 99.54

Polietal.[50] 109963 109522 545 441 0.90 99.60 99.50

Moraesetal.[51] N/R N/R N/R N/R N/R 99.22 99.73

Hamilton[52] N/R N/R N/R N/R N/R 99.80 99.80

Table2–TypicalfeatureofanormalECGsignal,witha

cardiacfrequencyof60beatsperminute(bpm)ofa

healthyadult.

Feature Normalvalue Normalvariation

Pwave 110ms ±20ms PQ/PRinterval 160ms ±40ms QRSWidth 100ms ±20ms QTinterval 400ms ±40ms AmplitudeofP 0.115mV ±0.05mV AmplitudeofQRS 1.5mV ±0.5mV STlevel 0mV ±0.1mV AmplitudeofT 0.3mV ±0.2mV Source:Ref.[1].

theRRinterval.TheRRintervalisthetimebetweentheRpeak ofaheartbeatwithrespecttoanotherheartbeat,whichcould beitspredecessororsuccessor.Withexceptionofpatientsthat utilizeapacemaker,thevariationsperceivedinthewidthof theRRintervalarecorrelatedwiththevariationsinthe mor-phologyofthecurve,frequentlyprovokedbyarrhythmias[1]. Thus,thefeaturesintheRRintervalhaveagreatcapacityto discriminatethetypesofheartbeatsandsomeauthorshave basedtheirmethodsonlyonusingtheRRintervalfeatures

[67–69]. Variationsofthis feature are usedto reducenoise

interferenceandareverycommon,e.g.,theaverageoftheRR intervalinapatientforacertaintimeinterval[70].

LinandYang[41]haveshownthattheuseofanormalized RR-intervalsignificantlyimproves the classificationresults. OnlynormalizedRR-intervalsareusedinthatworkandthe resultsarecomparabletothestate-of-the-artmethodseven undertheinter-patientparadigm.Doquireetal.[71]confirmed theefficiencyofnormalizedRR-intervalsbymeansoffeature selectiontechniques.

Otherfeaturesextractedfromtheheartbeatintervalsare alsofoundinliterature,suchasotherdistancesbetweenthe ﬁducialpointsofaheartbeat(herecalledECG-intervalsorECG segments),ascanbeseen inFig.5.Amongtheseintervals, theQRSinterval,orthedurationoftheQRScomplex,isthe mostutilized.Sometypesofarrhythmiasprovokevariations intheQRSinterval,makingitagooddiscriminatingfeature

[7,72].Itisworthmentioningthatthereexistotheralgorithms

availabletodeterminetheseﬁducialpoints,suchastheone proposedbyLagunaetal.[63].Table2displaysthestandard

valuesfortheseintervals,consideringahealthyhumanbeing withnocardiacabnormalities.

Features extracted from the domain of time/frequency togetherwiththefeaturesoftheRRintervalappearaspart ofthemethodsthatproducedthehighestaccuraciesin

litera-turetodate(seeTable7).Thesimplestwaytoextractfeatures

inthetimedomainistoutilizethepointsofthesegmented ECGcurve,i.e.,theheartbeat,asfeatures[73,74].However,the use ofsamplesofthecurve asfeaturesisatechniquethat isnotveryefﬁcient,sincebesidesproducingavectorofthe featureswithhighdimensions(dependingontheamountof samplesusedtorepresenttheheartbeat),itsuffersfrom sev-eralproblemsrelatedtothescaleordisplacementofthesignal withrespecttothecentralpoint(peakR).

Aimingatreducing thedimensionofthe featurevector, various techniqueshavebeen applieddirectlyon the sam-ples that represent the heartbeat (in the neighborhood of theRpeak)asprincipalcomponentanalysis(PCA)[75–77],or independentcomponentanalysis(ICA)[78–80],inwhichnew coefﬁcientsareextractedtorepresenttheheartbeat.Chawla

[81]presentsacomparativestudybetweentheuseofPCAand ICAtoreducethenoiseand artifactsoftheECGsignaland showedthatPCAisabettertechniquetoreducenoise,while ICAisbetteronetoextractfeatures.TheICAtechniqueenables statisticallyseparateindividualsourcesfromamixingsignal. TheECGisamixofseveralactionpotentialsandeachaction potential could bestrongly relatedto anarrhythmia class. TherationalebehindICAforECGheartbeat classificationis toseparatetheactionpotentialssourcesaswellasthenoise sources.ThePCAtechniqueseparatesthesourcesaccording totheenergycontributiontothesignal.Thestudypresented in[81]suggestthatnoisesourcesonthisbasehavelowenergy and are difficultto isolateand that the individual sources isolatedbyICAarepromisingfeaturesforECGclassification. Moreover,ithasbeenshownthatthe combinationofthese twotechniques,i.e.,PCAfornoisereductionandICAfor fea-tureextraction,canoffergreateradvantageswhencompared tousingonlyoneofthem.AnothertechniquebasedonPCA, theKernelPrincipalComponentAnalisys(KPCA),wasusedby Kanaanetal.[82].Inthatwork,acomparisonbetweenPCA andKPCAwasperformedanditwasconcludedthatKPCAis superiortothePCAtechniqueforclassifyingheartbeatsfrom theECGsignal.AccordingtoKallasetal.[83],KPCAperforms better,duetoitsnonlinearstructure.

(7)

Fig.6–Featurenumberreductionbymeansof interpolation.

Source:Ref.[7].

Özbay et al. [84] used clustering techniques directly in pointssampledfromthecurvetoreducefrom106samples to67clusters/points.Theauthorsalsousedaclustering tech-niquetoincreasethenumberoffeaturesto212,buttherewere nosigniﬁcantdifferencesintheresults.

Asletal.[85]usedGeneralizedDiscriminantAnalysis(GDA) toreducethedimensionsofthefeaturesoftheheartbeat inter-valtypetoclassifyrhythmicarrhythmias.Theyreportedan accuracycloseto100%forthistypeofarrhythmiausingthe MIT-BIHdatabase.However,theauthorsdidnottakecareto separatetheheartbeatsofthesamepatientusedduring train-ingand testing(intra-patientparadigm),whichisaserious concerndiscussedfurther.Theinter-patientparadigmshould beconsideredforamorerealisticscenario.

Simplertechniques,suchasinterpolation,havealsobeen usedtoreducethenumberofpointsrepresentingthe heart-beat.AnexampleofthistechniqueispresentedbydeChazal et al.[7], inwhich the heartbeat,originallyrepresented by 250samples(approximately600msofthecurve,sampledat 360Hz),was sub-dividedand presentedin18 samples(see

Fig.6).In theliterature,the sub-sampledECGwave isalso calledmorphologyormorphologicalfeatures.

Recently,randomprojectionshavealsobeenemployedfor

suchaim,asin[86,42].Huangetal.[42]showthatfeatures

extractedwithrandomprojectionsproducedresults compa-rabletothestate-of-the-artmethods,evenwhenconsidering theinter-patientparadigm.

Othertechniqueshavealsobeenemployed,suchaslinear predictivecoding[87], highorderaccumulates [88,89], clus-tering[84,90,91],correlationdimensionandlargestLyapunov exponent[92,93],Hermitetransform[94],localfractal dimen-sion[95].

Althoughvarioustechniqueshavebeenconsidered,most of the studies presented in literature use wavelet trans-formsandresearchersclaimthatthisisthebestmethodfor extractingfeaturesfromtheECGsignal[44,96,97].Thewavelet transformallowsinformationextractionfrombothfrequency andtime domains,differentfrom whatisusuallyachieved bythetraditionalFouriertransform[98]whichpermitsthe analysisofonlythe frequencydomain.Withinthetypesof wavelettransform,thediscrete wavelettransform(DWT)is

themostpopularforECGsignalclassiﬁcationduetoitseasy implementation.

BesidesDWT,continuouswavelettransform(CWT)hasalso beenusedtoextractfeaturesfromtheECGsignals[99],since itovercomessomeoftheDWTdrawbacks,suchasthe coarse-nessoftherepresentationand instability.However,CWTis notlargelyusedduetothefactthatitsimplementationand its inverse arenotavailableinstandard toolboxes(such as MATLABwaveletToolbox)andCWTshouldbecarefully dis-cretized for the use as a CWT analyzer. In addition, even thoughAddison[99]emphasizesthehighcomputationalcost asadisadvantageforusingCWT,ithasbeenemployed suc-cessfully evenonsimplemedicalequipmentsforatleasta decade.Finally,Addison[99]defendstheuseofDWT,together with CWT,because theyoffergain overthe methodologies used nowadays, inwhich the authors use onlyoneof the transforms.

AccordingtoGülerandÜbeyli[44],thechoiceofthemother wavelet function used for feature extraction is crucial for thefinalperformanceoftheclassificationmodel.Thischoice shouldbecarefullyanalyzedinordernottoloseimportant ECGsignaldetails.Besidesthechoiceofthemotherwavelet function, the orderoffilterand level ofdecomposition are parametersthatinfluencethefinalresultsofthearrhythmia classification.Daamoucheetal.[100]proposedtheuseofthe ParticleSwarmOptimization(PSO)techniqueforoptimizing theseparameters,and concludedthatthisprocess improve thefinalresults.

Intheliterature,variousstatisticalfeaturesextractedfrom the coefﬁcientsofwavelettransformare proposed,suchas mean, standard deviation, energy[44] and coefﬁcient

vari-ance[101].Thesefeatureshaveagreatadvantagesincethey

areimmunetothevariationsofﬁducialpointmarking.Some authorsusedtechniquestoreducethespaceofthefeatures afterapplyingthewavelettransform,suchasintheworkof

Songetal.[102]whocomparedthePCAandlinear

discrim-inant analysis (LDA) techniques for dimensional reduction after the use of wavelet transform. Wang et al. [103] and Polat & Günes¸ [104] alsoemployed PCAto reducefeatures formedbywaveletcoefficientsandalsoreportedasignificant improvementtheirresults.AccordingtoGülerandÜbeyli[44], theDaubechieswaveletsarethemostappropriatedmother waveletsforECGheartbeatclassification.Amongthem,the Daubechiesoforder2offersthebestaccuracy.

Althoughmanytechniqueshavebeenproposedtoextract andreducefeaturesfromECGsignalsaimingheartbeat classi-ﬁcation,onlyafewofthemhaveconsideredtheinter-patient paradigmasonecanseeinTable7.Therefore,itisdifﬁcultto evaluatewhetherfeaturesextractedwithPCA,ICA,GDAand othersareusefultodiscriminatepatientsorheartbeats.

Thevarianceoftheautocorrelationfunctionisconsidered tobeameasureofsimilarityorcoherencebetweenasignal anditsshiftedversion[101].Thistechniqueisusedfor fea-ture extractionfromwavelet coefﬁcients[101,37], andhave demonstratedtobeeffectiveinthediscriminationof arrhyth-micheartbeats.

Thevectorcardiogram(VCG)isarepresentationoftheECG signalintwodimensionsthatintegratesinformationfromtwo leads(seeFig.7).FeaturesextractedwithVCGwereusedin

(8)

Fig.7–VCGsetupusingtwoheartbeatsofMIT-BIH’srecord 202.

Source:AdaptedfromRef.[37].

classiﬁcationcategorizedasSupraventricularectopicbeat(SVEB) andVentricularectopicbeat(VEB)(arrhythmicheartbeats)can befavoredbyinformationfromleadsoftypeV1,V2 orV4. Becauseofthis,itisbelievedthatthefeaturesextractedby VCG(combinedwithleadsIIandV1)canhelptobetter dis-criminateminorityandimportantarrhythmicclassessuchas SVEBandVEB.

5.2. Featureselection

AccordingtoLlamedoandMartinez[37],manyauthorshave used techniques that reduce the feature space, but few have investigated techniques for feature selection in the contextofarrhythmiaclassiﬁcation.Llamedoand Martinez

[37]employed,forthe first timeinliterature,an algorithm forfeatureselectionbyusingfloatingsequentialsearch for arrhythmia classification. This method interchanges algo-rithmsexecuting forwardand backward searchestoobtain asetwiththemostrobustfeaturesandavoidlocaloptima inthe featurespace.Theproposedmethodachievedbetter resultsthanthestate-of-artmethodusingonlyeightselected features.

Recently,Maretal.[34]alsoperformedfeatureselectionby usingtheﬂoatingsequentialsearch[108].Inthatstudy,the authorsanalyzedasetofpossibilitiesofthefeatureselection, searchingforatrade-offbetweenthenumberoffeaturesand accuracy.Theaimofthatresearchwastomakeaspecially developedmethodadequateforambulatorymonitoring;that is,tobespeciallyusefulinrealworldapplications.Forsuch aim,anobjectivefunctionoptimized byafeatureselection method,wasespeciallydevelopedtobeanindicatorofthe

qualityofthearrhythmiaclassiﬁcationsfromanECGsignal. Inadditiontothelineardiscriminant (LD)classiﬁerusedin previousworks,Maretal.[34]employedamulti-layer percep-tron.However,neitheroftheseresultswerebetterthanthose proposedbydeChazaletal.[7]andtheworkofLlamedoand Martinez[37]intermsofaccuracy.Nonetheless,thefocusof

Maretal.[34]workwasonthemaintenanceofaccuracywith theuseofareducednumberoffeatures.

Feature selection techniques can bring various benefits for the classificationmethods, suchas the increaseof the generalizationpoweroftheclassificationalgorithmsandthe reductionofthecomputationalcost,duetothefactthatthey useasmallernumberoffeaturestoconstructthefinalmodel

[34].However,intheworksanalyzedinthissurvey,these tech-niqueswerelittleexplored.

Doquireetal.[71]comparewrapperfeatureselection tech-niqueagainstafilterfeatureselectiontechniqueandmore than200typesfeatures(dimensions)are consideredforthe task.Thewrapperfeatureselectionisusedwiththeweighted LDmodelusingaforward-backwardsearchstrategy.Thefilter techniqueemployedisthe mutualinformationin conjunc-tionwithrankingapproachandweightedSVM(SupportVector Machines).Accordingtotheauthors,resultshaveshownthat higherfiguresareobtainedwhenaverysmallnumberof fea-tures are selected. They stressed that the mostimportant featuresappearsareR-Rintervals,theamplitudeandlength oftheTwave,and2nd-orderstatistics.Alsotheyclaimedthat themutualinformationcriterionisapowerfultoolforfeature selectioninthisscenario.

AccordingtoZhangetal.[35],manyfeaturesare associ-atedwithmathematicalinterpretationanddonothaveaclear meaningtophysicians.Usually,theauthorsemployseveral combined featuresand theunderstandingofwhichfeature contributestodetectionofwhichclassofheartbeatisalsonot clearintheliterature.Aimingthat,Zhangetal.[35]proposed aheartbeat class-speciﬁcfeatureselectionscheme toallow the investigation of feature contribution for each arrhyth-mia/heartbeat class. Thus,wesuggest the incorporationof this approach on works aiming heartbeat classiﬁcation. It couldbringimportantcontributiontotheliteratureby allow-ingbetterunderstandingofcorrelationamongheartdiseases andfeaturesextractedfromECG.

State-of-art techniques for attribute selection, such as GeneticAlgorithms(GA) [109,110]and particle swarm opti-mization (PSO)[111,112]canalsoprovidepromisingresults andshouldbebetterinvestigatedinfutureworks.

6. Learning

algorithms

Oncethesetoffeatureshasbeendeﬁnedfromtheheartbeats, modelscanbebuiltfromthesedatausingartiﬁcialintelligence algorithmsfrommachinelearninganddataminingdomains

[113–115]forarrhythmiaheartbeatclassiﬁcation.

Thefourmostpopularalgorithmsemployedforthistask andfoundintheliteratureare:supportvectormachines(SVM)

[40,38,66], artiﬁcial neural networks (ANN) [34,116,69] and

linear discriminant (LD)[7,37,17],and ReservoirComputing With Logistic Regression (RC) [43]. Note that the state-of-the-art method aiming heartbeat classiﬁcation uses RC algorithm.

Due to their importanceforcardiac arrhythmic classiﬁ-cation, these four classiﬁers (SVM, ANN, LD, and RC) are discussedinthenextsubsections(Sections 6.1,6.2,6.3and

6.4).Then,Section6.5reviewsothertechniquesthatalsohave beenemployedtoarrhythmiaclassiﬁcation.

(9)

6.1. Supportvectormachines(SVM)

SVMisoneofthe mostpopularclassifiers foundin litera-tureforECG-basedarrhythmiaclassificationmethods. Park et al. [33] used SVM and validated the method according toAAMIstandardsandthe datasetsplitschemeproposed bydeChazalet al.[7]. Thesesame authorsused SVMina mock-hierarchyconfigurationtoresolvetheimbalanceofthe MIT-BIHdatabase,andreportedpromisingvalues.deLannoy etal.[32]managedtoovercometheimbalanceoftheMIT-BIH databasewithSVM,alternatingtheobjectivefunctionforeach class(WeightedSVM).Expressivegainswerereportedforthe SVEBandFclasses.

Variousapproaches withSVMvariationshavebeen pro-posed,suchasacombinationofthefuzzytheorytorefineSVM classification[117],combinedwithanensembleofclassifiers

[42],geneticalgorithmscombinedwithrestrictedfuzzySVM

[118]andleastsquaresSVM[104].Huangetal.[42]usedthe

SVMinahierarchicalmannerwithamaximumvotingstrategy andreportsigniﬁcantlyimprovements.

MoavenianandKhorrami[119]proposedtheuseofanew kernelfunctionforcapturingdatafromSVM.Inthatwork,it wasusedthesame methodologyforcomparingthe results obtainedfrom aSVMand aMultilayerPerceptronArtiﬁcial NeuralNetwork(MLP-ANN).WhileSVMwasmoreefﬁcientin executiontime,bothinthetrainingandinthetesting,MLP performedbetterintermsofaccuracy,Sensitivity(Se),positive prediction(+P)andfalsepositiverate(FPR).

SinceSVMpresents a negativebehavior forimbalanced classes,databasebalancingtechniquesforthetrainingphase, whicharelittleexploredforthisproblem,canbestudiedin futureresearch,asforexample,moresophisticatedsampling techniques,i.e.,SyntheticMinorityOver-samplingTechnique (SMOTE)[120].

6.2. Artiﬁcialneuralnetworks(ANN)

TheANNarchitecturesmostlyusedforarrhythmia classiﬁca-tionareMultilayerPerceptrons(MLP)andProbabilisticNeural Networks (PNN). According to Yu and Chen [101], models constructedwithPNNarecomputationallymorerobustand efﬁcientthanthetraditionalMLP.However,in[121,84,122,88], it wasproposed a hybridneuro-fuzzy networkmethods in ordertominimizethe problemsofMLP, increasingits gen-eralizationandreducingitstrainingtime.

ManyotherapproachesbasedonANNhavebeenproposed. Gülerand Übeyli[44]usedcombined neuralnetworksin ordertoobtainamoregenericmethodfromamore sophis-ticatedformofcross-validation.However,ofallthe articles mentioned in this study,only that of Mar et al. [34] used MLP with a more fair evaluation protocol by applying the patient division scheme proposed by de Chazal et al. [7]. Thusbyusingthereportedresultsintheworksofthe meth-odsthatutilizesANNasclassiﬁerisimpossible tomakesa faircomparison.Finally,Maret al.[34]comparedMLPwith LinearDiscriminants andfound thatMLPwas signiﬁcantly superior.

Combiningclassiﬁershadbeenlittleexploredforthetask inquestion.AccordingtoOsowskietal.[91],acombination

ofclassiﬁersnotonlyreducestheoverallerrorintheneural networks,butalsoreducestheincidenceoffalsenegatives.

6.3. Lineardiscriminants(LD)

TheLinearDiscriminantisastatisticmethodbasedonthe dis-criminantfunctions[114].Suchfunctionsareestimatedfrom atrainingsetofdataandtrytolinearlyseparatethefeature vector,beingadjustedbythe weightvector andabias.The criteriaforcalculatingtheweightvectorvariesaccordingto themodeladopted.In[7],theparameterswere determined usingthemaximum-likelihoodcalculatedfromtrainingdata. Lineardiscriminantsaretheclassifiersmoreusedin meth-odsthatfollowtheschemeproposedbydeChazaletal.[7]and recommendedbyAAMI.Theauthorsofthatresearchclaim thattheclassifierwaschosenforitssimplicityandforthefact thattheydidnotwanttoemphasistheclassifier,butinstead, theproposedfeatures.Amongstitsadvantages,LDcaneasily overcomeproblemsgeneratedbytheimbalanceofthe train-ingset(adifficultypresentedbyapproachesbasedonSVM). Whenusingtheschemeproposedin[7],itisagreatchallenge totuneSVMandMLPclassifierstoobtainpromising classifica-tioneffectivenessfortheminoritySVEBandVEBclasses(see

Table9).Moreover,theLDclassiﬁerrequireslesstrainingtime,

ifcomparedtoSVMandMLP,asitisnotiterative.Thatis,it simplycalculatesstatisticsfromthetrainingdataandthen, theclassiﬁcationmodelisdeﬁned.

6.4. Reservoircomputingwithlogisticregression(RC) AccordingtoRodanandTi ˇno[123],reservoircomputing mod-els are dynamical models aiming to process a time series signal in two parts: represent the signal through a non-adaptabledynamicreservoirandadynamicreadoutfromthe reservoir.MoredetailsregardingRCcanbefoundin[124].

The state-of-the-art method for heartbeat classiﬁcation uses RC [43]. According toEscalona-Moran et al.[43], their approachusesasimplenonlineardynamicalelementsubject toadelayedfeedbackwhereeachpointoftheECGsignalis sampledandheldduringonedelaytimeandthenmultiplied by abinary random mask. Thelearningprocess is accom-plished with logistic regression. Thetechnique appearsto berobusttothe classimbalance ofthe dataset.Besides, it achievesthebestresultsintheliteraturetodate(seeTable7). Inaddition,theauthorsalsoclaimthatthetechniqueis suit-abletoimplementinhardwareduetoitslowcomputational cost,whichallowsthedevelopmentofrealtimeapplications forheartbeatclassiﬁcation.

6.5. Othertechniques

Manyothermethodsforarrhythmiaclassiﬁcationhavebeen developed using other machine learning and data mining algorithms,suchasdecisiontrees[125,126,68],nearest

neigh-bors[127–129],clustering[73,130,131],hiddenMarkovmodels

[132,133], hyperbox classiﬁers [105], optimum-path forest

[134],conditionalrandomﬁelds [8]and rules-basedmodels

[135,67,136].

Algorithms with a lazy approach, such as the k Near-est Neighbors(kNN),arenotmuchusedfortheproblemof

(10)

arrhythmiaclassification,sincetheirefficiencyisintimately connectedtopreviousknowledgetoperformtheclassification ofeachsamplethatisrepresentedbythecompletetraining set,whichleadstoahighcomputationalcostduringthe test-ingphase.Thiscostcaninvalidateitsusefordiagnosisinreal time.MishraandRaghav[95]usedaclassifierbasedonkNN andreportedpromisingresults,howeverthecomputational costwasnotmentioned.Inotherworks,alsobasedonkNN, intheliterature[127,92,128,137,129],noonepresentedamore fairevaluationprotocolforcomparisonofmethodsastheone proposedbydeChazaletal.[7],andnoonealsofollowedthe AAMIrecommendations.Inaddition,thecomputationalcost ofthesemethodswasnotinvestigated.

Clusteringtechniquesare widelyusedalong with Artiﬁ-cialNeuralNetworks.AccordingtoÖzbayetal.[84],theycan improvethegeneralizationcapacityoftheneuralnetworks and diminish the learning time. Some works used unsu-pervised clustering techniques to agglomerate all of the heartbeatsintherecordofagivenpatientintoclusters[131]

and the final classification of each cluster, i.e., the heart-beats ofthatgroup, isthen defined byahumanspecialist

[138,130,73].Otherworksinthissameway[139,140,17],using

lineardiscriminantasclassiﬁersandfairevaluationschemes present promisingresults whichare reliable for real-world applications.Itisimportanttonotethatthissemi-automatic (orpatient-speciﬁc paradigm)and promisingapproachstill dependsonahumanspecialist.

HMMiswidelyusedtoaudioandspeechsignalanaysis andrecognition[141,142].Coastetal.[132]usedHMMforthe arrhythmiaclassiﬁcationproblem, other studies haveused thistechniquetoanalyzeECGsignals.Forinstance,Andreao

etal.[143]validatedtheuseofHMMforECGanalysisin

med-icalclinics(realworld).

The Optimum-path Forest (OPF) classifier was used for arrhythmiaclassificationforthefirsttimebyLuzetal.[134]. In that work, the OPF performance, interms of computa-tionalcostandoverallaccuracy,wascomparedtootherthree classifiers:Bayesian,SVMandMLP.Experimentsshowedthat OPFobtained,inaverage,comparableresults,revealingitasa promissoryapproach.

Methodsthatuseadecisiontreeallowaninterpretationof thedecisionsmadebythemodel[68].However,thistypeof methodisnotefficientforcontinuousfeatures(belongingto asetofrealnumbers)[144,145]andfeaturevectorsoflarge dimensions[146].Thus,methodsthatusedecisiontrees con-sideronlyafewfeatures.Forexample,in[68],onlythefeatures intheRRintervalwereusedinthedecisiontree.Meanwhile thehyperboxclassifiers,besidesprovidinghighlevelof inter-pretationofthe classificationrules,are alsomore efficient forhigher dimensionfeaturevectors [105]. Mertet al.[147]

usedacombinationtechniqueofbagginganddecisiontree. Accordingtotheauthors,theBaggedDecision Tree demon-stratedgreateraccuracyandabettercapacitytodiscriminate theclasses.

The methods with the greatest interpretation level are theonesthatuseasetofrules.Thesetofrulespresented byTsipouraset al.[135,67,136] was obtainedtogether with cardiologistsand arerelatedtoamorphological tachogram forarrhythmicevents.Methodsconstructedinconjunction withrulesusuallypresentaworserperformance,intermsof

effectiveness,whencomparedtoothermethodsproposedin literature.However,notestusingafairercomparisonscheme, suchastheoneproposedbydeChazaletal.[7],andthe rec-ommendationsofAAMI,wasdonewithmethodsthatusea setofrules.ThissubjectisdiscussedindepthinSection 8.

Usingafewdiscriminativefeaturesfrom previousworks

[71,37],deLannoyetal.[8]proposedtheuseofweighted

Condi-tionalRandomFieldsfortheclassificationofarrhythmiasand comparedtheapproachwithSVMsandLDs.Theexperiments demonstrated thattheproposedmethodobtainspromising resultsforthe minority arrhythmicalclasses (SVEBeVEB). However, the relatively low efficiency forthe normalclass (80%)representsaproblem whenusedinreallifescenario (inter-patientparadigm),sincemanyhealthyheartbeatswill beclassifiedasarrhythmic.

7. Databases

and

the

AAMI

standard

Variousdatabasesarecomposedofcardiacheartbeatgrouped inpatientsrecordsfreelyavailablethatpermitsthecreationof astandardizationfortheevaluationofautomaticarrhythmia classiﬁcationmethods.Thisstandardizationwasdeveloped byAAMIandisspeciﬁedinANSI/AAMIEC57:1998/(R)2008[10]

anddeﬁnedtheprotocoltoperformtheevaluationstomake suretheexperimentsarereproducibleandcomparable.

Theuseofﬁvedatabasesisrecommendedbythe standard-ization:

• MIT-BIH:TheMassachusettsInstituteofTechnology–BethIsrael HospitalArrhythmiaDatabase(48recordsof30mineach); • EDB: The European Society of Cardiology ST-T Database (90

recordsof2heach);

• AHA:TheAmericanHeartAssociationDatabaseforEvaluationof VentricularArrhythmiaDetectors(80recordsof35mineach); • CU:TheCreightonUniversitySustainedVentricularArrhythmia

Database(35recordsof8mineach);

• NST: The Noise Stress Test Database (12 records of ECGof 30mineach,plus3recordswithnoiseexcess);

Themost representative database forarrhythmiais the MIT-BIH,andbecauseofthis,ithasbeenusedformostofthe publishedresearch.Itwasalsothefirstdatabaseavailablefor thisgoalandhasbeenconstantlyrefinedalongtheyears[148]. Themajorityoftheheartbeatsrecordedinthesedatabases haveannotationsassociatedwiththetypeofheartbeatorthe events.Theseheartbeat annotations,asmuchfortheclass andforthefiducialpoints(e.g.,pointR,maximumamplitude oftheheartbeat)are fundamentalforthedevelopmentand evaluationofautomaticarrhythmiaclassificationmethods.

TheANSI/AAMIEC57:1998/(R)2008standardalsospecifies howannotationsshouldbedoneinthedatabases.Anexample canbeseeninFig.8,inwhichthereistheleadIIattheupper partofthefigure,leadV1atthelower,andsomeannotations inthecenter.Noteworthyisthefactthatitisrecommended thatrecordsofpatientsusingpacemakersshouldnotbe con-sidered.Inthisdatabase,4patients/recordshavethisproperty anditsrespectiveheartbeatsshouldberemoved.Inaddition, segmentsofdatacontainingventricularflutterorfibrillation (VF)shouldalsobeexcludedfromtheanalysis.

(11)

Fig.8–ExampleofannotationsinaMIT-BIHdatabase. Source:Ref.[107].

Althoughvarioustypesofcardiacarrhythmiasexist,AAMI recommends that onlysome types should be detected by equipment/methods.Thereare15recommendedclassesfor arrhythmiathatareclassiﬁedinto5superclasses:Normal(N), Supraventricularectopicbeat(SVEB),Ventricularectopicbeat (VEB),Fusionbeat(F)andUnknownbeat(Q).Table3illustrates the15classesandtheirsymbols,aswellasthehierarchyof the5groups(superclasses).

ThemeasuresrecommendedbyAAMIforevaluating meth-odsare:Sensitivity(Se),Positivepredictivity(+P),Falsepositive rate(FPR)andOverallaccuracy(Acc).SensitivityandPositive Predictivityarealsoknowninliteratureasrecallandprecision, respectively;theoverallaccuracycanbestronglydistortedby theresultsofthemajorityclass.Inthisway,theﬁrst three measuresarethemostrelevantforcomparingthemethods, sincetheclassesfortheheartbeattypesareextremely imbal-ancedinavailabledatabases.

Calculationofthemeasuresisbasedonthedeﬁnitions pre-sentedinTable4.Notethatinsections(a),(b),and(c)ofTable4, formulasandschemastocomputeSe,+P,FPRandAccaregiven

Table3–Principaltypesofheartbeatspresentinthe

MIT-BIHdatabase.

Group Symbol Class

N Nou. Normalbeat

Anyheartbeat L Leftbundlebranchblockbeat notcategorized R Rightbundlebranchblockbeat asSVEB,VEB, e Atrialescapebeat

ForQ j Nodal(junctional)escapebeat SVEB A Atrialprematurebeat

a Aberratedatrialprematurebeat Supraventricular J Nodal(junctional)prematurebeat ectopicbeat S Supraventricularprematurebeat VEB V Prematureventricularcontraction Ventricular E Ventricularescapebeat

ectopicbeat

F F Fusionofventricular

Fusionbeat andnormalbeat

Q Pou/ Pacedbeat

Unknownbeat f Fusionofpacedandnormalbeat U Unclassiﬁablebeat

forV,SandNclasses,respectively.Observethataccordingto

thistable,itisnotnecessarytopenalizeVEB+Pwiththefalse

positivesFvandQv(ashighlightedintheschemaofTable4(a)),

meanwhile,forSVEB+P,Qv(ashighlightedintheschemaof

Table4(b))alsodoesnotneedtoenterthecalculation.

Thestandardalsosuggeststhattheresultsshouldbe pre-sented inaglobalmanner,consideringthateach heartbeat hasthesameweight(grossstatistics)andinpersamplebasis. AsetofresultsisexempliﬁedinTable5.

Next,webrieflydiscussthefivedatabasesrecommended bythe standard,presentingthenumber ofrecords,sample frequency,resolutionandfinalityofeach.

7.1. MIT-BIH

Thisdatabase1 _is_presented_in_majority _of_the _publications

foundinliterature.Itisuniquesinceitcontemplatesthefive arrhythmiagroupsproposedbyAAMIasdescribedinTable3. Thisdatabasecontains48recordsofheartbeatsat360Hz forapproximately30minof47differentpatients.Eachrecord containstwoECGleadsandinthemajorityofthemthe prin-cipallead(leadA)isamodificationofleadII(electrodeson thechest).Theotherlead(leadB)isusuallyleadV1, modi-fied,butinsomerecords,thisleadisknowntobeV2,V5orV4

[107].Generally,leadAisusedtodetectheartbeats,sincethe

QRScomplexismoreprominentinthislead.LeadBfavorsthe arrhythmicclassiﬁcationofthetypesSVEBandVEB[107].More informationregardingthisdatabasecanbefoundin[148].

7.2. EDB

TheEDBdatabase2_is_a_collection_of₉₀_records_acquired_from

79subjects,sampledat250Hzwith12-bitresolution.These recordswereextractedfrom70men(between30and84years old) and8women (between55 and 71years old). Asallof thesesubjectsweresufferingfromaspeciﬁccardiacdisease (i.e.,myocardialischaemia),thedatabasewasoriginallybuilt toallowST-segmentandT-waveanalysis.

Theheartbeatswererecordedforatwohourdurationand each ofthemcontainstwosignals(i.e.,twoleads).Two car-diologistsmadetheannotationsfortherecordandtheAAMI standardwasused.Moreinformationregardingthisdatabase canbefoundin[149].

7.3. AHA

TheAHAdatabase3_consists_of₁₅₅_records,_each _one

com-posedoftwoleads,sampledat250Hzwith12-bitresolution. Eachrecordingisthreehourslongandonlytheﬁnal30min havebeenannotated.Thedatabasewascreatedtoevaluate ventriculararrhythmiadetectors.However,thedatabasedoes notdifferentiatenormalsinusrhythmfromsupraventricular ectopicbeats(SVEB).

1 _The_complete_information_regarding_the_database_as_well_as itsusageanddataannotation/labellingcanbefoundin

http://www.physionet.org/.

2 _The_EDB_database_can_be_obtained_in_[107]_.

(12)

Table4–Calculationsformethodevaluations.(a),(b),and(c)highlightthecalculationofmeasuresforV,S,andN,

respectively.

Source:Ref.[7].

Abbreviations:Acc:Accuracy;F:Fusionheartbeatgroup(superclass);FPR:Falsepositiverate;N:Normalheartbeatgroup(superclass);+P: Pos-itivepredictivity;Q:Unknownheartbeatgroup(superclass);Se:Sensitivity;Sp:Speciﬁcity;S&SVEB:Supraventricularectopicheartbeatgroup (superclass);V&VEB:Ventricularectopicheartbeatgroup(superclass);TN:Truenegative;andTP:Truepositive.

Table5–ExhibitionexampleofresultsaccordingtotheAAMIstandard.

Record Acc N SVEB VEB F Q

Se/+P/FPR Se/+P/FPR Se/+P/FPR Se/+P/FPR Se/+P/FPR

101 99.5 99.7 99.8 60.0 33.3 12.5 0.4 – – 0.0 – – 0.0 0.0 – 0.0 106 72.5 97.5 100.0 0.0 – 0.0 27.5 0.0 – 0.0 – – 0.0 – – 0.0 108 97.2 98.3 99.7 21.7 75.0 6.4 2.5 0.0 – 0.0 0.0 – 0.0 – – 0.0 109 95.9 97.4 99.5 27.5 – 0.0 3.7 0.0 – 0.0 0.0 – 0.0 – – 0.0 ... ... ... ... ... ... ... ... ... ... ... ... ... ... 215 3.0 3.1 99.0 0.6 100.0 0.1 97.1 0.0 – 10.0 90.0 – 60.0 – – 0.0 220 98.5 99.4 99.0 21.3 78.7 87.1 0.6 – – 0.0 – – 0.0 – – 0.0 223 79.8 98.6 94.7 20.0 86.3 13.2 16.3 0.0 – 30.0 0.0 – 0.0 – – 0.0 Gross 75.5 83.0 98.1 14.4 45.8 4.3 19.2 0.0 0.0 4.5 50.0 0.0 10.0 0.0 0.0 0.0 7.4. CU

TheCUdatabase4_is_composed_of₃₅_eight-minute_ECG

recor-dings,sampledat250Hzwith12-bitresolution.Thedatabase was intended to evaluate algorithms aiming at detecting episodes of sustained ventricular tachycardia, ventricular flutter,andventricularfibrillation.Itprovidesreference anno-tation files to aid users to locate these events on the recordings.Moreinformationregardingthisdatabasecanbe foundin[150].

7.5. NSD

The NSD database5 _includes ₁₂ _half-hour _ECG _recordings

and3half-hournoise recordings.Thenoiseinsertedinthe recordingsistypicalinterferencesfoundinambulatorycare services,suchasbaselinewander,muscleartefact(EMG)and

4 _The_CU_database_and_the_annotation_ﬁles_can_be_obtained_in

[107].

5 _The_NSD_database_and_its_annotation_ﬁles_can_be_obtained_in

[107].

electrodemotionartefact.AccordingtoGoldbergeretal.[107], the electrode motionartefact isconsidered tobe themost troublesome,sinceitcanbeeasilymisinterpretedasectopic beats.Also,itcannotbeeasilyremovedbyﬁlters.

TheECGrecordingsavailableintheNSDdatabasewere cre-atedbased ontwoclean recordingsfrom MIT-BIH (118and 119).Thenoise wasartiﬁciallyinserted inthe signals.This databaseismoredetaileddescribedin[151].

8. Heartbeats

selection

problem

for

evaluation

of

methods

TheAAMIstandardspecifiesaprotocolfortestsand evalua-tion ofarrhythmiaclassificationmethods.Italsostipulates which databases should be used. However, it does not specify which patients/heartbeats should be used to con-structthemodeltobeclassified(trainingphase)andwhich patients/heartbeatsshouldbeusedforevaluationmethods,

i.e.,thetestingphase,whichmayrenderbiasedresults.For instance, deChazal et al.[7] demonstratedthat the useof heartbeatsfromthesamepatientforboththetrainingandthe testingmakestheevaluationprocessbiased.Thisisbecause

(13)

Table6–Heartbeatdistributionbyclassesofsets/partsasproposedbydeChazaletal.[7].

Set N SVEB VEB F Q Total

DS1 45866 944 3788 415 8 51021

DS2 44259 1837 3221 388 7 49712

DS1+DS2 90125 2781 7009 803 15 100733

themodelstendtolearn theparticularitiesofthepatient’s

heartbeatduringthetraining,obtainingexpressivenumbers

duringthetest(verycloseto100%).Aspreviouslymentioned,

this heartbeat division protocol is called in the literature

intra-patientschemeorparadigm[8,42].However,ina

clini-calenvironment,afullyautomaticalgorithm/methodwillﬁnd heartbeatsofpatientsdifferentfromthosetheyusedto learn-inginthetrainingphase.

Intendingtospecifyaprotocol,theworkofdeChazaletal.

[7]proposed adivisionofthe heartbeats fromthe MIT-BIH databaseinto twosetsso thatthedatabase becomesmore coherentwithreality.Theﬁrstsetiscomposedofall heart-beatsofrecords:101, 106, 108, 109,112, 114, 115,116, 118, 119,122,124, 201,203,205, 207,208,209, 215,220,223and 230,calledDataset1(DS1).Whilethesecondiscomposedof allheartbeatsofrecords:100,103,105,11,113,117,121,123, 200,202,210,212, 213,214,219,221, 222,228,231,232, 233 and234, calledDataset 2(DS2).Theauthors onlyusedDS1 toconstructtheclassiﬁcationmodel,whileDS2wasreserved for evaluation. In this way, they guaranteed that the cre-atedmodelhadnocontactwiththeheartbeatspertainingto DS2,i.e.,heartbeats fromDS1andDS2comefromdifferent individuals.Suchdivisionprotocoliscalledintheliterature inter-patientschemeorparadigm[7,8,42].Notethatonlythe MIT-BIHdatabasewasusedforthecreationofthesets,since itistheonlyoneindicatedbytheAAMIstandardthat contem-platesall5ofthesuperclassesforarrhythmias.

AccordingtodeChazaletal.[7],theserecordswere pri-marilydividedintwoparts:oddandevennumberedrecords. Theﬁnalrecordselectionwasachievedbyexchangingsome oftherecordsbetweenthepartssoastobalancetheclasses. TheheartbeatdistributionofthesetscanbeseeninTable6. Observethatthetwosetshaveapproximatelythesame num-berofheartbeatsperclass,withapproximately100thousand heartbeats.Itisworthmentioningthatrecords201and202 arefromthesamepatient,butareindifferentsets.Theother recordspertaintoonlyonepatient.

deChazaletal.[7]concludedthatforamorerealistic evalu-ation,theDS1setmustbeusedfortrainingandtheDS2setfor testing,makingheartbeatclassificationasignificantlymore difficulttask,andconsequently,reducingtheperformanceof thepresentedclassifyingmethod.Theyalsoconcludedthat theminorityclasses (SVEBand VEB),wherethemost prob-lematicarrhythmiasarefound,sufferedmorewiththistype ofprotocol.

Tables 7and 8 lists the main works,considered byus,

publishedinliterature,groupedaccordingtotheschemeof heartbeat selection: intra-patient, where heartbeats of the samepatientsprobablyappearinthetrainingaswellasin thetestingdataset;andthesecondscheme, whereauthors tooktheprecautiontoconstructandevaluatethe classiﬁca-tionusingheartbeatsfromdifferentpatients(inter-patient), followingtheprotocolproposedby[7].

Worksthatdonotfitintointer-patientcategory,donot per-mitfaircomparisonswiththeresultsintheliterature,oncea greatmajorityoftheauthorsdidnotfollowthesameprotocol fortheevaluations.AsonecanseeinTable8,itisalsodifficult toassesswhichtechniquecontributetoheartbeat classifica-tion,sincemethodswithdifferentapproachesachievesvery high(>98%)accuracies.Thus,thereportedresultsgroupedin

Table8cannotbetakenintoconsiderationfromaclinicalpoint

ofview,sincethereportedvaluesbytheseworksareprobably differentinareallifescenariointermsofaccuracy.

Unfortunately,thegreatmajorityoftheworksinthe liter-aturedoesnotconcernonfollowingthedivisiondeﬁnedin

[7]oranyotherinter-patientprotocolthatimposedthe non-usageofheartbeatsfromthesamepatientinthetrainingand testing[160],asshowninTable8.

Aimingtostandardizetheevaluationprocessconsideringa clinicalpointofviewandAAMIrecommendations,wesuggest futureworkstofollowaworkﬂow6_:

1. Databaseselection:

• UseMIT-BIHARRDBwithinter-patientschemeproposed in[7]toallowunbiasedliteraturecomparison;

• UseINCARTdatabasetoassessgeneralizationpowerof themethod,asproposedin[37];

2. Preprocessing. Run all process with at least 2 ﬁltering scheme besides the proposed ﬁltering method by the authors:

• Signalﬁlteringasproposedin[7]toallowliterature com-parison;

• UsetheRawSignal,i.e.,noﬁltering.Thisshouldworkas agroundtruth;

3. Segmentation:

• AddjittertoRlocationannotationasproposedin[66]to testtherobustnessofthemethodagainstsegmentation errors;

4. Featureextraction:

• Usefeatureselectiontoreportwhichproposedfeatures improvetheresults;

• Useaclass-orientedfeatureselectiontoassess which featureismoresuitabletowhichdiseaseasproposedin

[35].Thiscouldresultinanimportantcontributiontothe literature;

5. Classiﬁcation:

• Duringtraining,useak-patientcrossvalidationtodeﬁne modelparametersasproposedin[7].

• Investigate thedatabase imbalanceimpact onchosen classiﬁer,byreportingresultswithandwithoutuseof techniquestocompensatetheimbalance;

6 _The_proposed_workﬂow_for_the_evaluation_process_is_an importantcontributionofthissurveywork.

(14)

Table7–Methodswhichusedinter-patientparadigm.

Work Featureset Classiﬁer Effectiveness

deChazaletal.[7] ECG-Intervals, WeightedLD Acc=83%;

Morphological SeN=87%;+PN=99%;

SeS=76%;+PS=38%;

SeV=77%,+PV=82%;

SoriaandMartinez[31] RR-Intervals, WeightedLD Acc=90%;

VCG,morphological SeN=92%,+PN=85%;

+FFS SeS=88%,+P=93%;

SeV=90%,+P=92%

LlamedoandMartinez[37] Wavelet, WeightedLD Acc=93%;

VCG SeN=95%;+PN=98%;

+SFFS SeS=77%;+PS=39%;

SeV=81%,+PV=87%;

Maretal.[34] TemporalFeatures, WeightedLD Acc=89%;

Morphological, MLP SeN=89%;+PN=99%;

statiscialfeatures SeS=83%;+PS=33%;

+SFFS SeV=86%,+PV=75%;

#Bazietal.[40] Morphological, SVM Acc=97%(DS1)

Wavelet IWKLR,DTSVM Acc=92%(DS2)

Luzetal.[134] features SVM, SeN=84%SpSVEB=18%

proposedin ANN, SpVEB=72%

[102,101,79] Bayesian,

[70,44] OPF

Yeetal.[38] Morphological,Wavelet, SVM Acc=86.4%

RRinterval,ICA, SeN=88%;+PN=97%;

PCA SeS=60%;+PS=53%;

SeV=81%,+PV=63%;

deLannoyetal.[32] ECG-Intervals, weightedSVM Acc=83%;

morphological, SeN=80%;

HOS, SeS=88%;

HBFcoeﬁcients SeV=78%;

Parketal.[33] HOS,HBF HierarchicalSVM Acc=85%;

SeN=86%;

SeS=82%;

SeV=80%;

Zhangetal.[35] RR-intervals, CombinedSVM Acc=86%;

morphologicalfeatures, SeN=89%;+PN=99%;

ECG-intervalsandsegments SeS=79%;+PS=35%;

SeV=85%,+PV=92%;

Escalona-Moranetal.[43] Rawwave RC Acc=98%;

SeN=96%;+PN=91%; SeS=79%;+PS=96%; SeV=96%;+PV=99%;

#Huangetal.[42] Randomprojection EnsembleofSVM

RR-intervals SeN=99%;+PN=95%;

SeS=91%;+PS=42%;

SeV=94%,+PV=91%;

$LinandYang[41] normalizedRR-interval weightedLD Acc=93%;

SeN=91%;+PN=99%;

SeS=81%;+PS=31%;

SeV=86%,+PV=73%;

deLannoyetal.[8] RR-intervals,ECG-segments weightedCRF Acc=85%;

morphological,HBF,HOS AccN=79%;

AccS=92%;

AccV=85%;

ZhangandLuo RR-intervals, CombinedSVM Acc=87%;

morphologicalfeatures, SeN=88%;+PN=98%;

ECG-intervalsandsegments, SeS=74%;+PS=59%;

waveletscoeff. SeV=88%,+PV=82%;

ANN,ArtiﬁcialNeuralNetwork;PCA,PrincipalComponentAnalysis;FFS,FloatingFeatureSelection;ICA,IndependentComponentAnalysis; BPNN,BackPropagationNeuralNetwork;HBF,HermiteBasisFunction;HOSC,highorderstatisticscummulants;LD,LinearDiscriminants;SFFS, Sequentialforwardﬂoatingsearch;IWKLR,ImportanceWeightedKernelLogisticRegression;CRF,ConditionalRandomFields;RC,Reservoir Computing;$Authorsoptimizetheirresultfor3classes(N,SVEB,VEB);#Whereconfusionmatrixwasnotgiven,somevaluescouldnotbe computed.

(15)

Table8–MethodswhichusedIntra-patientparadigm.

Work #cl. Featureset Classiﬁer Effectiveness

Chenetal.[152] 2 RR-interval Setofrules Acc=95%

Lagerholmetal.[138] 16 HBF,SOM clustering Acc=98%

DokurandOlmez[98] 10 Fourier,Wavelet+FSDP MLP,RCE, Acc=96%

NovelhybridNN

OsowskiandLinh[88] 6 HOSC fuzzyNN Acc=96%

Tsipourasetal.[135] 9 RR-interval Deterministicautomata Acc=96%

Mehmet[122] 4 HOSC,Wavelet Min.Dist,kNN,Bayes Acc=98%

CristovandBortonal[106] 2 Heartbeat-Intervals,VCG NN Acc=99%

GulerandUbeyli[44] 4 Wavelets(statistics) CombinedNN Acc=96%

Songetal.[102] 6 Waveletcoef.,LDA SVM Acc=99%

RR-Intervals

Karimifardetal.[153] 7 HBF kNN Acc=99%

Özbayetal.[84] 10 Raw-wave MLP,FuzzyCluster, Acc=99%

FCNN

Tsipourasetal.[136] 4 RR-interval FuzzyExpertSystem Acc=96%

Bortolanetal.[105] 2 VCGandMorphological FuzzyClustering Acc=99% hyperbox+GA

Ubeyli[154] 4 DWT SVM,ECOC Acc=99%

YuandChen[101] 5 ICA,RR-interval PNN Acc=99%

CeylanandOsbay[76] 10 DWT FCM,NN Acc=99%

YuandChen[101] 6 Wavelet(statistics) PNN Acc=99%

RR-interval

MinhasandArif[137] 6 Wavelet,RR-interval,PCA kNN Acc=99%

Linetal.[96] 7 MorletWavelet AWN Acc=90%

KorurekandNizam[127] 6 RR-interval,ECG-segments ACO-basedCluster, Acc=94% kNN

YuandChou[79] 8 RR-interval,ICA PNN,BPNN Acc=98%

Asletal.[85] 6 HVR,GDA SVM Acc=100%

Ceylanetal.[90] 10 PCA,DWT FCMT2,ANN Acc=99%

Wenetal.[73] 16 RR-interval,raw-wave SOCMAC-basedCluster Ac=98%

YuandChou[80] 8 ICA SVM Acc=98%

Kimetal.[77] 6 RR-interval,PCA ELM Acc=98%

Yeetal.[70] 15 Wavelet,ICA, SVM Acc=99%

PCA,RR-interval

OzbayandTezel[74] 10 ECG-wave NNAAF Acc=98%

MishraandRaghav[95] 6 LFD NearestNeighbor Acc=89%

KorurekandNizan[127] 6 RR-interval,QRS-width, ACO,kNN Acc=90%

Wavelet,PCA

Lanataetal.[128] 6 HOS MOG,kNN Acc=85%

Yehetal.[131] 5 Morphological,RR-interval clustering Acc=94%

QFS

Kallasetal.[83] 3 KPCA SVM Acc=97%

Khazaee[155] 3 Heartbeatintervals PSO+SVM Acc=97%

morphologyamplitudes

Wangetal.[103] 8 PCA,LDA PNN Acc=99%

KumarandKumaraswamy[69] 3 RR-intervals CART,RBF, Acc=92%

MLP,IOAW-FFNN

Chenetal.[156] 6 RR-intervals SVN,NN Acc=100%

Mertetal.[147] 6 RR-intervals,HOS, BaggedDecisionTree Acc=99%

2ndorderLPCcoeff.

AhmedandArafat[157] 11 Heartbeatintervals MLP,SVM,TreeBoost Acc=98% morphologyamplitude,HOS

Sarfrazetal.[78] 11 RR-intervals,QRSpower BPNN Acc=99%

ICAcoeff.

Tranetal.[158] 7 RR-intervals,HBF Ensembleofclassiﬁers Acc=98%

AlickovicandSubasi[159] 5 autoregressive(AR)modeling SVM,MLP, Acc=99% RBF,kNN

NN,NeuralNetwork;PCA,PrincipalComponentAnalysis;GDA,GeneralizedDiscriminantAnalyses;ECOC,Errorcorrectingoutputcodes;NNAAF, NeuralNetworkAdaptativeActivationFunction;ACO,AntColonyOptimizationbasedclustering;ICA,IndependentComponentAnalysis;FCM, FuzzyC-Means;AWN,adaptivewaveletnetwork;PNN,ProbabilisticneuralNetwork;BPNN,BackPropagationNeuralNetwork;PSO,Particle swarmoptimization;CWT,ContinuesWaveletTransform;DWT,DiscreteWaveletTransform;DCT,DiscreteCosineTransform;FCMT2,Fuzzy C-Meanstype2;MOG,MixtureofGaussian;QFS,Qualitativefeatureselection;HMM,HiddenMarkovmodeling;LPC,linearpredictivecoding; BME,Burgsmaximumentropy;SOM,self-organizingmaps;HBF,HermiteBasisFunction;HOSC,Highorderstatisticscummulants;HOS,Higher orderstatistics;LD,LinearDiscriminants;SOCMAC,Self-organizingcerebellarmodelarticulationcontrollernetwork;ELM,ExtremeLearning Machine;LFD,Localfractaldimension;LDA,lineardiscriminantanalysis.