Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Maxime Voisin; Yichen Shen; Alireza Aliamiri; Anand Avati; Awni Hannun; Andrew Ng

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arXiv · 1811.07774

Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Maxime Voisin·Yichen Shen·Alireza Aliamiri·Anand Avati·Awni Hannun·Andrew Ng

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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 signals. Continuous and accurate detection of AF from PPG has the potential to transform consumer wearable devices into clinically useful medical monitoring tools.

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Maxime Voisin, Yichen Shen, Alireza Aliamiri, Anand Avati, Awni Hannun, Andrew Ng. 2018-11-27. Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning. https://arxiv.org/abs/1811.07774

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