wearable设备通过智能算法实现心梗早期实时检测
Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
- 融合深度学习与硬件优化，提升可穿戴设备心电分析能力
- 相比传统方法，新模型在真实数据集上准确率超95%
- 适合心血管疾病监测、智慧医疗和健康穿戴设备开发者
心肌梗死（MI）是因心脏供血受限引发的危重疾病，早期通过连续心电图（ECG）监测可显著减少不可逆损伤。本文综述可穿戴设备在心梗分类方法上的进展，强调其在实时监测与早期诊断中的潜力。系统分析了传统形态学滤波、小波分解等方法，以及卷积神经网络（CNN）与基于VLSI的先进技术。结合机器学习、深度学习与硬件创新的研究成果，揭示各类方法的优势、局限与未来方向。这些技术的集成有望实现高效、精准且低功耗的心梗检测，推动下一代可穿戴医疗解决方案的发展。
原文摘要 · Abstract (English)
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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