Decision Support Systems for Cardio Vascular Disease using Deep Learning Techniques

Abstract

In thelate20thcentury,theurgentneedtoprocesstheexponentiallyrisingcollecteddata about cardiovasculardiseasesledtheresearcherstoexplorethepotentialresultsofdecision support systems(DSS).ProvidedthattheECGsignalplaystheimportantroleintheclinical diagnosis ofheartdiseases,variousmethodsundertookthistimeseriesdataanalysisasastep forwardtowardimplementingtheirinterpretationmodels. This thesispresentstheprincipalpartsofECGanalysesandtheinnovativestate-of-the- art classificationmethodsoftheaforementioneddata.Intheinterestofclassifyingthemost common typeofCVDs“arrhythmias”.Precededbygeneralconceptsandimportantinformation to preparethereadertocomprehendthefollowingmethodologiesandparadigmsused. In theliterature,MostmethodsstartedbyacquiringECGdatafromthepubliclyavailable datasets, mainlyMIT-BIHarrhythmiadatabase.Followingthisphase,apreprocessingofthe signal alongwithfeatureextraction,selection,and/ortransformation.Finally,theclassification and evaluationprocess,yieldingsuccessmeasuresoftheadoptedmethod. Manyresearcherstooktwomainapproachestoclassify:1)individualheartbeats,2)longer- term ECGsignals.Likewise,usingthesameordifferentpatientdatafortrainingandtestsets as validationmethod.Asaresult,theyusedmanymechanismsproducingexcellentresults, although somepronetoerrorand/orbiased. This topicofresearchstillneedsattentioninsomeareaswhereambiguityfallsupon.More- overtosomeclinicalimplicationforevaluatingtheDSS.