Atrial fibrillation classification and detection from ECG recordings

Basit Öğe Kaydı
dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorGunduz, Ali Fatih
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:53:16Z
dc.date.available2024-08-04T20:53:16Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: Atrial fibrillation (AF) heart rhythm disorder is investigated under two topics: Persistent AF (PeAF) and Paroxysmal AF (PAF). Diagnosis and detection of PeAF is relatively easier than PAF and PAF generally remains unrecognized. It is observed that a significant number of studies in the literature focused on detection of AF.Methods: In this study, four different approaches are examined for AF detection. The first one is based upon spectral features obtained from windowed ECG signals. In the second approach, distances between successor R peaks are used as features. Then in the third approach, P waves are detected from the ECG signals by using R peak positions and then the model is trained by those P waves. In those three approaches a deep learning ar-chitecture with bidirectional long short-term memory (BiLSTM) network is used. Finally, in the fourth approach, a convolutional long short-term memory (CLSTM) model with convolution and LSTM layers is used for classi-fication. The data set used in this work is obtained from 4th China Physiological Signal Challenge-2021.Results: As the result of experimental studies, it is seen that classification approach based on spectral features provided the best training accuracy (0.9788) and classification based on P wave detection provided the best test accuracy (0.8765).Significance: This study compares PeAF and PAF detection and classification methods based on deep learning models using different approaches. BiLSTM networks being capable of reflecting time sensitive features of ECG, appeared to be superior to CNN and LSTM cascades.en_US
dc.identifier.doi10.1016/j.bspc.2022.104531
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85144625953en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.104531
dc.identifier.urihttps://hdl.handle.net/11616/101075
dc.identifier.volume82en_US
dc.identifier.wosWOS:000920654500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAtrial fibrillationen_US
dc.subjectECGen_US
dc.subjectSignal processingen_US
dc.subjectClassificationen_US
dc.subjectDeep learningen_US
dc.subjectCNNen_US
dc.subjectLSTMen_US
dc.titleAtrial fibrillation classification and detection from ECG recordingsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu