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B751_9145

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AMulti-scaleDeepBottleneckNetworkforECGSmallSampleClassification1stDaweiLiSouth-CentralUniversityforNationalitiesCollegeofelectronicsandInformationEngineeringWuhan,Chinaleedavidhust@outlook.com3rdCongLiuSouth-CentralUniversityforNationalitiesCollegeofElectronicsandInformationEngineeringWuhan,Chinaliucong16948@foxmail.com2ndLiqiLiaoSouth-CentralUniversityforNationalitiesCollegeofelectronicsandInformationEngineeringWuhan,Chinaliaoliqi2022@163.com4thXiaoweiXuGuangdongAcademyofMedicalSciencesGuangdongProvincialPeople’sHospitalGuangzhou,Chinaxiao.wei.xu@foxmail.comAbstract—Electrocardiogram(ECG)playsacrucialroleinthediagnosisofcardiovasculardiseases.Inrecentyears,theextensiveuseofdeeplearningalgorithmprovidesanopportunitytoimprovetheefficiencyofdiagnosis.However,theexistingstudieshavemainlyfocusedontheECGclassificationusingsingle-scaleinformation.Formostsmallsamplesapplications,thelimitedtrainingdatacannotprovideseveralsingle-scaleinformationtodemonstratethedistributionofdiseaseclasses.Inthispaper,weproposeamulti-scaledeepbottlenecknetwork,whichcanbeusedtodetectcardiovasculardiseasesespeciallyarrhythmiasinsmallECGdataset.Inthismodel,wedesignedthreebranchescombiningthebottleneckstructurestoextractECGfeaturesatdifferentscales.EachbottleneckstructureisaddedwithaSqueezeandExcitationNetwork(SE-Net)toenhancetheeffectivefeaturechannelsandsuppresstheinefficientones.Toverifytheperformanceofthemodel,wecomparedthe12-leadsECGdatasetcollectedfromGuangdongProvincialPeople’sHospitalwiththatofthepublicMIT-BIHarrhythmiadataset.Forthesetwodatasets,98.2%and98.7%accuracyareachieved,respectively,whichcanexceedtheperformanceofcurrentexistingmethods.ExperimentalresultsshowthatthisworkisespeciallybeneficialtoimprovetheclassificationaccuracyofsmallECGdatasetsandcanbeevenappliedtoothertasksofsmalldatasetsuchasobjectdetection.IndexTerms—Smallsampleclassification,Cardiovascular,Multi-scale,BottleneckStructureI.INTRODUCTIONCardiovasculardiseaseisoneofthemaincausesofhu-mandeathandposesahugefactorleadingtotheriseofmedicalandhealthcarecosts.Datashowthatthenumberofpatientsworldwidewithcardiovasculardiseasesincreasedfrom271millionin1990to523millionin2019[1].Asanimportantgroupofcardiovasculardiseases,arrhythmiacancauseheartfailureandsuddendeath[2].Thus,regularmonitoringturnsouttobeparticularlyimportant.Clinically,regularmonitoringisachievedbyECGwhichcanreflecttheelectricalactivityofcardiacexcitementandiscommonlyperformedformedicalscreeningofmanycardiacdiseases,suchasdiagnosingarrhythmiaandcoronaryheartdisease[3][4].Withthedevelopmentofdeeplearningtechnique,therequirementforautomatedECGanalysisisincreasinglyrised,forallowing24-hourmonitoringandprovidingadditionaldiagnosticinformation.AlthoughlargeamountsofECGdatacanbeobtainedthroughcontinuousmonitoring,theECGannotationforsupervisedmodeltrainingislimitedintheexperiencedcardiologists,whichwillinevitablyleadtohighlaborcosts.Therefore,inordertoimprovetheclassificationperformanceofsmallsampleECGsignals,thedesignofalgorithmshasbecomeatoppriorityinpractice.ThedesignofalgorithmsforECGsmallsampleclassifi-cationshouldsatisfytheneedtomaximizetheclassificationaccuracywithlimitedsupervisedinformation.Specifically,amodelislearnedwithasmallnumberofavailabletrainingsamplestoidentifytheclassesofECGs.However,thetra-ditionalmachinelearningalgorithms[5][6][7][8]donotsolvethesmallsampleclassificationproblemwell.Themainchallengecomesfromthatthemodellosesalargeamountofinformationduringfeatureextraction,whichleadstoalowclassificationaccuracyforsmallsamples.Deeplearningalgorithmscanachieveaneffectivecombinationoffeatureextractionandclassificationthroughend-to-endlearning,thussolvingtheproblemofinformationlossinmanualfeatureextraction.However,mostofcurrentworksonlyconsidersingle-scaleinformationandignorecomplementaryinforma-tionfromotherscales[9].Asaresult,forsmallsamples,thelimitedtrainingdatadoesnotprovidesufficientsingle-scaleinformationtodemonstratethedistributionofdiseaseclasses.Howtoextractmoremeaningfulinformationfromlimitedsamplestoimprovetheclassificationperformance?InspiredbytheDMSFNetmodelproposedbyRuxinetal[10],weemployamulti-scalefeatureextractionapproachtosolvethisproblem.Multi-scalefeatureextractionnotonlyallowstoobservetheamplitudeandlocalstatisticalinformationinECGsignals,butalsoenablestobetterencodethespacinginformationbetweendifferentwavesandmorphologicalfeatures,suchasQRSduration,P-RintervalandR-Rintervaletc.Therefore,themethodofmulti-scalefeatureextractionisbeneficialtomake

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