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ResNet and BiLSTM Networks for Myocardial Infarction Detection

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  • Rashedur Rahman
  • S A Sabbirul Mohosin Naim

Abstract

This study evaluates two deep learning models, ResNet1D and CNN-BiLSTM-Attention, for automated myocardial infarction detection using 12-lead ECG signals from the PTB-XL dataset. Both models were trained using class-weighted learning and data augmentation. ResNet1D achieved 96.50% accuracy, 92.88% precision, 92.63% recall, and 92.75% F1-score, while CNN-BiLSTM-Attention achieved 95.40% accuracy, 88.85% precision, 93.77% recall, and 91.08% F1-score. The results demonstrate that deep learning can accurately identify myocardial infarction from ECG recordings, with ResNet1D providing the best overall performance and CNN-BiLSTM-Attention offering a compact and effective alternative.

Suggested Citation

  • Rashedur Rahman & S A Sabbirul Mohosin Naim, 2025. "ResNet and BiLSTM Networks for Myocardial Infarction Detection," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(6), pages 722-728, December.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i6:id:2086
    DOI: 10.32628/CSEIT26123378
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123378
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