Comparative Evaluation of BiLSTM, ResNet-1D, and Transformer Architectures for Automated ECG Arrhythmia Classification

Abstract

Cardiovascular disease remains a leading cause of global mortality, and continuous electrocardiogram (ECG) monitoring through wearable devices offers a practical pathway for the early detection of life-threatening arrhythmias. This study presents a controlled, comparative evaluation of three deep learning architectures—Bidirectional Long Short-Term Memory (BiLSTM), one-dimensional Residual Network (ResNet-1D), and the Transformer Encoder—for the automated classification of cardiac arrhythmias from single-lead ECG beat segments drawn from the MIT-BIH Arrhythmia Database. A total of 17,966 heartbeat segments were extracted and conditioned through a three-stage preprocessing pipeline comprising Butterworth band-pass filtering, Symlet-4 wavelet denoising, and Z-score normalization. The severe class imbalance inherent to arrhythmia data was corrected using the Synthetic Minority Over-sampling Technique (SMOTE), producing a balanced corpus of 50,000 samples distributed evenly across the five heartbeat classes defined by the Association for the Advancement of Medical Instrumentation (AAMI). All three architectures were trained under an identical regime and evaluated on a held-out test set using accuracy, precision, recall, weighted F1-score, the Matthews Correlation Coefficient, Cohen’s kappa, and macro-averaged ROC-AUC. ResNet-1D achieved the strongest overall performance, with a test accuracy of 98.74%, a Matthews Correlation Coefficient of 0.9843, and a macro-averaged ROC-AUC of 0.9995, outperforming BiLSTM (97.80% accuracy) and the Transformer Encoder (97.50% accuracy) across every metric in the evaluation suite. The results indicate that residual convolutional architectures provide the most favourable balance of aggregate accuracy and minority-class sensitivity for single-beat ECG classification, offering practical guidance for the design of accurate, wearable-deployable cardiac monitoring systems.

Keywords

• ECG classification
• deep learning
• BiLSTM
• ResNet-1D
• Transformer
• SMOTE
• wearable monitoring
• arrhythmia detection

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