Deep hybrid neural network for automatic classification of heart arrhythmias using 12-lead electrocardiograms
Sultan D. Baikuvekov M. Omarov B. Kassenkhan A. Nuralykyzy S. Zhekambayeva M.
February 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 1287 - 296 pp.
This research introduces a novel convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid network for the automatic classification of heart arrhythmias using 12-lead electrocardiograms (ECGs). By merging the spatial feature extraction capabilities of CNNs with the temporal precision of BiLSTM networks, our approach sets a new standard in cardiac diagnostics. The proposed model was tested against the comprehensive CPSC2018 dataset, demonstrating superior performance with an accuracy of 90.67%, precision of 93.27%, recall of 96.35%, and an F-score of 94.78%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating spatial and temporal data analysis, offering a robust and reliable tool for medical practitioners. This study represents a significant advancement in automated ECG analysis, paving the way for improved diagnosis and treatment of heart diseases, and contributing to enhanced patient outcomes in cardiac care.
Bidirectional long short-term memory , Convolutional neural network , Deep learning , Heart disease , Medical signal processing
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Department of Information Systems, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Mathematical and Computer Modeling, Faculty of Computer Technology and CyberSecurity, International Information Technology University, Almaty, Kazakhstan
School of Digital Technology, Narxoz University, Almaty, Kazakhstan
Department of Software Engineering, Institute of Automation and Information Technologies, Satbayev University, Almaty, Kazakhstan
Department of Information Systems
Department of Mathematical and Computer Modeling
School of Digital Technology
Department of Software Engineering
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