Myocardial infarction (MI), commonly known as a heart attack, is a critical health condition caused by restricted blood flow to the heart. Early-stage detection through continuous ECG monitoring is essential to minimize irreversible damage. This review explores advancements in MI classification methodologies for wearable devices, emphasizing their potential in real-time monitoring and early diagnosis. It critically examines traditional approaches, such as morphological filtering and wavelet decomposition, alongside cutting-edge techniques, including Convolutional Neural Networks (CNNs) and VLSI-based methods. By synthesizing findings on machine learning, deep learning, and hardware innovations, this paper highlights their strengths, limitations, and future prospects. The integration of these techniques into wearable devices offers promising avenues for efficient, accurate, and energy-aware MI detection, paving the way for next-generation wearable healthcare solutions.

本研究针对心肌梗死的早期检测问题进行了深入探讨，强调了可穿戴设备在实时监测和早期诊断中的潜力。通过对传统方法与最新技术的比较，揭示了机器学习、深度学习及硬件创新在心肌梗死检测中的优势和未来前景，为下一代可穿戴医疗解决方案开辟了新路径。

基于可穿戴设备的心肌梗死检测与分类的进展：全面综述