Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Abstract

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.