Energy-efficient Real-time Myocardial Infarction Detection on Wearable Devices
Energy-efficient Real-time Myocardial Infarction Detection on Wearable Devices
复制标题
可穿戴设备上的节能实时心肌梗死检测
DOI：
10.1109/embc44109.2020.9175232
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. A. Faruque
中科院分区：
文献类型：
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作者：
Nafiul Rashid;M. A. Faruque
心肌梗塞（MI）是致命的心脏病，是死亡的主要原因。 MI的沉默和经常性的性质需要每天通过可穿戴设备进行实时监控。可穿戴设备上的实时MI检测需要快速，节能的解决方案，以实现长期监测。在本文中，我们提出了使用二元卷积神经网络（BCNN）的MI检测方法，该方法快速，节能，胜过可穿戴设备上最先进的工作。我们从Phancionet验证了我们方法的性能。对真实硬件的评估表明，与最先进的工作相比，我们的BCNN更快，可达到12倍的能源效率。
Myocardial Infarction (MI) is a fatal heart disease that is a leading cause of death. The silent and recurrent nature of MI requires real-time monitoring on a daily basis through wearable devices. Real-time MI detection on wearable devices requires a fast and energy-efficient solution to enable long term monitoring. In this paper, we propose an MI detection methodology using Binary Convolutional Neural Network (BCNN) that is fast, energy-efficient and outperforms the state-of-the- art work on wearable devices. We validate the performance of our methodology on the well known PTB diagnostic ECG database from PhysioNet. Evaluation on real hardware shows that our BCNN is faster and achieves up to 12x energy efficiency compared to the state-of-the-art work.