Diagnosing heart disease is a complex and critical task that requires extracting meaningful patterns from large electrocardiogram (ECG) datasets. As the demand for faster and more reliable diagnostic tools increases, deep learning has emerged as a transformative solution, enabling automated ECG interpretation and reducing the risk of human error. However, traditional models often face challenges such as high computational complexity and limited adaptability to the dynamic nature of ECG signals.

In this study, we investigate the potential of transformer-based architectures to overcome these limitations and enhance classification performance. We explore two distinct strategies: the first employs a standalone transformer encoder to classify ECG signals into five categories—Normal beats (N), Unknown beats (Q), Ventricular ectopic beats (V), Supraventricular ectopic beats (S), and Fusion beats (F)—achieving an accuracy of 91%. The second approach integrates a Convolutional Neural Network (CNN) with the transformer encoder, where the CNN extracts relevant features that are subsequently refined and classified by the transformer, resulting in a significantly higher accuracy of 98%.

These findings demonstrate the effectiveness of transformer models, particularly when combined with CNNs, in improving the precision and robustness of ECG signal classification. This research contributes to the growing field of AI-assisted healthcare and highlights the promise of hybrid deep learning frameworks in supporting more efficient and accurate cardiac diagnostics.