Coronary Artery Disease Classification Using Deep Neural Networks

• Diogo Casal Ferreira PUC Minas
• Alexei Manso Correa Machado PUC Minas / UFMG

DOI: https://doi.org/10.5753/sbcas.2025.6951

Abstract

Coronary artery disease (CAD) is one of the leading causes of global mortality, highlighting the need for more effective early diagnosis methods. This study compares LSTM, Bi-GRU, and Transformer neural networks in classifying electrocardiograms (ECG) into three categories: normal, inferior myocardial infarction (IMI), and anterior myocardial infarction (AMI), using the PTB-XL dataset. Results show that Transformers offer faster training (average of 35 minutes), while LSTM and Bi-GRU achieve higher accuracy (91% and 92%, respectively). The study also contributes to improving ECG data preprocessing and training for cardiac diagnosis classification.

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