Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(2019)
摘要
We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a wrist-worn device. Using a 50-layer convolutional neural network, we achieve a test AUC of 95% and show robustness to motion artifacts inherent to PPG signals. Continuous and accurate detection of AF from PPG has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.
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关键词
ambulatory, atrial fibrillation, convolutional neural network, deep learning, ppg
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