Showing 1–1 of 1 results for author: Avati, A

Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Authors: Maxime Voisin, Yichen Shen, Alireza Aliamiri, Anand Avati, Awni Hannun, Andrew Ng

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% and show robustness to motion artifacts inherent to PPG… ▽ More 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. △ Less

Submitted 27 November, 2018; v1 submitted 12 November, 2018; originally announced November 2018.

Comments: Equal contribution by Maxime Voisin and Yichen Shen