Advancing heart disease diagnosis with vision-based transformer architectures applied to ECG imagery

Abstract

Cardiovascular disease, a critical medical condition that affects the heart and blood vessels, requires timely detection for effective clinical intervention. This includes coronary artery disease, heart failure, and myocardial infarction. Our goal is to improve the detection of heart disease through proactive interventions and personalized treatments. Early identification of at-risk individuals using advanced technologies can mitigate disease progression and reduce adverse outcomes. Using recent technological advancements, we propose a novel approach for heart disease detection using vision transformer models, namely Google-Vit, Microsoft-Beit, Deit, and Swin-Tiny. This marks the initial application of transformer models to image-based electrocardiogram (ECG) data for the detection of heart disease. The experimental results demonstrate the efficacy of vision transformers in this domain, with BEiT achieving the highest classification accuracy of 95.9% in a 5-fold cross-validation setting, further improving to 96.6% using an 80-20 holdout method. Swin-Tiny also exhibited strong performance with an accuracy of 95.2%, while Google-ViT and DeiT achieved 94.3% and 94.9%, respectively, outperforming many traditional models in ECG-based diagnostics. These findings highlight the potential of vision transformer models in enhancing diagnostic accuracy and risk stratification. The results further underscore the importance of model selection in optimizing performance, with BEiT emerging as the most promising candidate. This study contributes to the growing body of research on transformer-based medical diagnostics and paves the way for future investigations into their clinical applicability and generalizability.