AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals

Basit Öğe Kaydı
dc.contributor.authorEfe, Enes
dc.contributor.authorYavsan, Emrehan
dc.date.accessioned2024-10-29T17:43:37Z
dc.date.available2024-10-29T17:43:37Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractWithin the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias. © 2024 the Author(s).
dc.identifier.doi10.3934/mbe.2024259
dc.identifier.endpage5880
dc.identifier.issn1547-1063
dc.identifier.issue4en_US
dc.identifier.pmid38872562
dc.identifier.scopus2-s2.0-85193257523
dc.identifier.scopusqualityQ2
dc.identifier.startpage5863
dc.identifier.urihttps://doi.org/10.3934/mbe.2024259
dc.identifier.urihttps://hdl.handle.net/20.500.11776/12521
dc.identifier.volume21
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAmerican Institute of Mathematical Sciences
dc.relation.ispartofMathematical Biosciences and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectarrhythmia detection
dc.subjectBiLSTM
dc.subjectcardiology
dc.subjectcardiovascular disease
dc.subjectclass imbalance
dc.subjectelectrocardiography
dc.titleAttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals
dc.typeArticle

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu