A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM

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dc.authoridKUNCAN, Fatma/0000-0003-0712-6426
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Fatma
dc.contributor.authorTekin, Ramazan
dc.date.accessioned2024-12-24T19:25:04Z
dc.date.available2024-12-24T19:25:04Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractElectrocardiogram (ECG) is widely used as a diagnostic method to identify various heart diseases such as heart failure, cardiac and sinus rhythms. The ECG signal analyzes the electrical activity of the heart and shows waveforms that help detect heart irregularities. A new approach is suggested for automatic identification of congestive heart failure (CHF) and arrhythmia (ARR). In this study, long short-term memory neural networks (LSTM) were used to classify ECG signals by combining LSTM and angle transform (AT) methods. The AT uses the angular information of the neighbor signals on both sides of the target signal to classify ECG signals. The new signals obtained as a result of AT conversion vary between 0 and 359. Histogram of new signals determines the inputs to the LSTM method. LSTM uses histograms to distinguish between three different conditions: ARR, CHF, and normal sinus rhythm (NSR). The proposed approach is tested on ECG signals received from MIT-BIH and BIDMC databases. The experimental results have shown that the proposed method, AT + LSTM, has achieved high success rate of classifying ECG signals. The success rate in classifying CHF, ARR, and NSR ECG signals for 70-30% training sets was observed as 98.97%. Further experiments were conducted for varying training-testing dataset ratio to demonstrate the robustness of the proposed approach, and success rates are observed between 98.56 and 100%. Another experiment regarding different values of the dR and dL distance parameters of the AT model has shown that the performance of the proposed method increases while increasing the distance value. The success rates from increasing the distance value were obtained between 98.97 and 100%. To show the effect of segment lengths of ARR, NSR, and CHF signals on classification success, these signals were divided into segments of 10,000, 5000, and 1000 lengths. Achieved success rates ranged from 97.75 to 98.97%. Considering the results, high results were observed with the AT + LSTM approach, which is generally recommended in all scenarios.
dc.identifier.doi10.1007/s13369-022-06617-8
dc.identifier.endpage10513
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85124761205
dc.identifier.scopusqualityQ1
dc.identifier.startpage10497
dc.identifier.urihttps://doi.org/10.1007/s13369-022-06617-8
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6237
dc.identifier.volume47
dc.identifier.wosWOS:000757174700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal For Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectECG
dc.subjectLSTM
dc.subjectAngle transformation
dc.subjectArrhythmia
dc.subjectCongestive heart failure
dc.subjectNormal sinus rhythm
dc.subjectFeature extraction
dc.titleA New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM
dc.typeArticle

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