Transformer-Based ECG Classification for Early Detection of Cardiac Arrhythmias

Sunnia Ikram^1*Amna Ikram²Harvinder Singh³Daler Ali¹Sajid Naveed¹Isabel De La Torre⁴Henry Fabian Gongora⁵Thania Candelaria⁶

• ¹Islamia University of Bahawalpur, Bahawalpur, Pakistan
• ²Government Sadiq College Women University, Bahawalpur, Bahawalpur, Punjab, Pakistan
• ³College of Technology, Chandigarh Group of Colleges, Mohali, Punjab, India
• ⁴Department of Signal Theory and Communications, Higher Technical School of Engineering, University of Seville, Seville, Spain
• ⁵Universidad Europea del Atlántico, Santander, Cantabria, Spain
• ⁶Laboratorio de Genética Molecular Humana, Centro Universitario Regional Litoral Norte Salto, Universidad de la República, Salto, Uruguay

Electrocardiogram (ECG) classification plays a critical role in the early detection and monitoring of cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures. Principal Component Analysis (PCA), correlation analysis, and feature engineering are applied to retain the most informative features. To assess the discriminative quality of the selected features, t-distributed Stochastic Neighbor Embedding (t-SNE) is used for visualization, revealing clear class separability in the transformed feature space. The refined dataset is then input to a Transformer-based model trained with optimized loss functions, regularization strategies, and hyperparameter tuning. The proposed model demonstrates strong performance on the MIT-BIH benchmark dataset, showing results consistent with or exceeding prior studies. However, due to differences in datasets and evaluation protocols, these comparisons are indicative rather than conclusive. The model effectively classifies ECG signals into categories such as Normal, AtrialPremature Contraction (APC), Ventricular Premature Contraction (VPC), and Fusion Beats. These results underscore the effectiveness of Transformer-based models in biomedical signal processing and suggest potential for scalable, automated ECG diagnostics. However, deployment in real-time or resource-constrained settings will require further optimization and validation.