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A Multistage Deep Learning Algorithm for Detecting Arrhythmia

dc.contributor.authorAltan, Gökhan
dc.contributor.authorAllahverdi, Novruz
dc.contributor.authorKutlu, Yakup
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T22:02:50Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T22:02:50Z
dc.date.issued2018
dc.identifier.citationAltan, G., Allahverdi, N., Kutlu, Y. (2018). A Multistage Deep Learning Algorithm for Detecting Arrhythmia. 1st International Conference on Computer Applications and Information Security, ICCAIS 2018, art. no. 8441942. https://doi.org/10.1109/CAIS.2018.8441942en_US
dc.identifier.urihttps://doi.org/10.1109/CAIS.2018.8441942
dc.identifier.urihttps://hdl.handle.net/20.500.12508/473
dc.description1st International Conference on Computer Applications and Information Security, ICCAIS 2018 -- 4 April 2018 through 6 April 2018 -- -- 138933en_US
dc.description.abstractDeep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MIT-BIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification performances of the DBN are 94.15%, 92.64%, and 93.38%, for accuracy, sensitivity, and selectivity, respectively. © 2018 IEEE.en_US
dc.description.sponsorshipAcknowledgment N.Allahverdi thanks the KTO Karatay University for its support of this work.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionof10.1109/CAIS.2018.8441942en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArrhythmiaen_US
dc.subjectDeep Belief Networken_US
dc.subjectDeep learningen_US
dc.subjectECGen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationInformation Systemsen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationInterdisciplinary Applicationsen_US
dc.subject.classificationHeart Arrhythmia | Electrocardiograph | Supraventricular Premature Beaten_US
dc.subject.otherECG arrhythmiaen_US
dc.subject.otherNeural-networken_US
dc.subject.otherClassificationen_US
dc.subject.otherRecognitionen_US
dc.subject.otherMorphologyen_US
dc.subject.otherMixtureen_US
dc.subject.otherPCAen_US
dc.subject.otherBayesian networksen_US
dc.subject.otherDiscrete fourier transformsen_US
dc.subject.otherDiseasesen_US
dc.subject.otherElectrocardiographyen_US
dc.subject.otherHigher order statisticsen_US
dc.subject.otherLearning algorithmsen_US
dc.subject.otherSecurity of dataen_US
dc.subject.otherWavelet decompositionen_US
dc.subject.otherClassification performanceen_US
dc.subject.otherDeep belief network (DBN)en_US
dc.subject.otherDeep belief networksen_US
dc.subject.otherLayer-wiseen_US
dc.subject.otherMulti stageen_US
dc.subject.otherSupervised trainingsen_US
dc.subject.otherWavelet packet decompositionen_US
dc.titleA Multistage Deep Learning Algorithm for Detecting Arrhythmiaen_US
dc.typeconferenceObjecten_US
dc.relation.journal1st International Conference on Computer Applications and Information Security, ICCAIS 2018en_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümüen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorAltan, Gökhanen_US
dc.contributor.isteauthorKutlu, Yakupen_US
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Conference Proceedings Citation Index- Scienceen_US


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