Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
Maxime Voisin, Yichen Shen, Alireza Aliamiri, Anand Avati, Awni Hannun and Andrew Ng
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% in presence of motion artifacts inherent to PPG signals. Such continuous and accurate detection of AF has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.
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