Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review

TL;DR

This comprehensive review discusses recent advancements in detecting and classifying myocardial infarction using wearable devices, focusing on machine learning, deep learning, and hardware innovations for real-time, energy-efficient monitoring.

Contribution

It critically analyzes traditional and cutting-edge methods, highlighting their strengths, limitations, and future potential in wearable MI detection technologies.

Findings

Deep learning techniques improve detection accuracy

Hardware innovations enable energy-efficient real-time monitoring

Integration of AI and hardware advances enhances wearable MI diagnosis

Abstract

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.