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Article · Bulletin of Electrical Engineering and Informatics · November 22, 2024

Deep hybrid neural network for automatic classification of heart arrhythmias using 12-lead electrocardiograms

Daniyar Sultan, Meirzhan Baikuvekov, Batyrkhan Sultanovich Omarov, Aray M. Kassenkhan, Saltanat Nuralykyzy, Maigul Zhekambayeva

Aray Kassenkhan
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Abstract

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.

ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmiasArtificial Intelligence in Healthcare12-lead ECGconvolutional neural networksbidirectional LSTM

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