Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
It addresses the problem of early MI detection for healthcare applications, but as a review, it is incremental in synthesizing existing research rather than presenting new findings.
This review paper examines advancements in myocardial infarction (MI) detection and classification methods for wearable devices, focusing on traditional and modern techniques like CNNs and VLSI-based approaches, and highlights their potential for efficient and accurate real-time monitoring.
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.