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

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

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.

Keywords


Bidirectional long short-term memory; Convolutional neural network; Deep learning; Heart disease; Medical signal processing

Full Text:

PDF


DOI: https://doi.org/10.11591/eei.v14i1.8285

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).