A new approach for congestive heart failure and arrhythmia classifiication using downsampling local binary patterns with LSTM

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dc.contributor.authorAkda, Sueleyman
dc.contributor.authorKuncan, Fatma
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-12-24T19:32:57Z
dc.date.available2024-12-24T19:32:57Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractElectrocardiogram (ECG) is a vital diagnosis approach for the rapid explication and detection of various heart diseases, especially cardiac arrest, sinus rhythms, and heart failure. For this purpose, in this study, a different perspective based on downsampling one-dimensional-local binary pattern (1D-DS-LBP) and long short-term memory (LSTM) is presented for the categorization of Electrocardiogram (ECG) signals. A transformation method named 1D-DS-LBP has been presented for Electrocardiogram signals. The 1D-DS-LBP method processes the bigness smallness relationship between neighbors. According to the proposed method, by downsampling the signal, the histograms of 1D local binary patterns (1D-LBP) calculated from the obtained signal groups are collected and included as a reference to the long short-term memory structure. The long short-term memory structure has been applied to 1D-DS-LBP conversion applied ECG signals with both unidirectional and bidirectional. To test the proposed approach, ECG signals of three (3) different states of congestive heart failure (CHF), arrhythmia (ARR), and normal sinus rhythm (NSR) consisting of 972 signals were used. Signals were taken from the MIT-BIH and BIDMC databases. Experiments were carried out in various scenarios. We observed that the success rate of the proposed approach obtained very high classification accuracies compared to other studies in the literature. The obtained ECG diagnostic performance values varied between 96.80% and 99.79%. Based on this, this approach has a high potential to have a wide field of study in medical applications.
dc.identifier.doi10.55730/1300-0632.3930
dc.identifier.endpage2164
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85142182265
dc.identifier.scopusqualityQ2
dc.identifier.startpage2145
dc.identifier.trdizinid1142545
dc.identifier.urihttps://doi.org/10.55730/1300-0632.3930
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1142545
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7897
dc.identifier.volume30
dc.identifier.wosWOS:000884407400010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectECG
dc.subjectLSTM
dc.subject1D-DS-LBP
dc.subjectarrhythmia
dc.subjectcongestive heart failure
dc.subjectnormal sinus rhythm
dc.titleA new approach for congestive heart failure and arrhythmia classifiication using downsampling local binary patterns with LSTM
dc.typeArticle

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