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Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
[article]
2018
arXiv
pre-print
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
arXiv:1811.07774v2
fatcat:3tdztno5fbd4lhl2vzperitt7e