Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

author: Maxime Voisin, Stanford University
published: March 2, 2020,   recorded: August 2019,   views: 5
Categories

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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 page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: