Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
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 meth...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2411.18451 |