Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
published: March 2, 2020, recorded: August 2019, views: 8
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !