Why the study?
Does a 50-layer convolutional neural network accurately detect Atrial Fibrillation episodes from ambulatory wrist-worn photoplethysmograms?
Does a 50-layer convolutional neural network accurately detect Atrial Fibrillation episodes from ambulatory wrist-worn photoplethysmograms?
A 50-layer convolutional neural network can accurately detect atrial fibrillation from ambulatory wrist-worn PPG signals with an AUC of 95%.
May enable wearable AF detection in free-living settings; leaves open prospective validation before clinical use.
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.
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Shen et al. (2019) studied this question.
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