Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results.

翻译：心脏病，又称心血管疾病，是一种普遍且危急的医学状况，其特征是心脏和血管功能受损，可导致冠状动脉疾病、心力衰竭、心肌梗死等多种并发症。及时准确地检测心脏病在临床实践中至关重要。早期识别高危个体能够实现主动干预、预防措施和个性化治疗策略，从而减缓疾病进展并减少不良结局。近年来，由于先进技术与计算方法的整合，心脏病检测领域取得了显著进展，包括利用大量临床和生理数据提高诊断准确性和风险分层的机器学习算法、数据挖掘技术及预测建模框架。在本研究中，我们提出使用前沿技术（即视觉Transformer模型）从心电图图像中检测心脏病。这些模型包括Google-Vit、Microsoft-Beit和Swin-Tiny。据我们所知，这是首次聚焦于通过基于图像的心电图数据并采用Transformer模型等前沿技术来检测心脏病的尝试。为验证所提框架的贡献，我们将视觉Transformer模型的性能与最新研究进行了比较。实验结果表明，所提框架展现出卓越的分类效果。