The increasing popularity of smartwatches as affordable and longitudinal
monitoring devices enables us to capture photoplethysmography (PPG) sensor data
for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in
AF detection from PPG signals comes from the inherent noise in the smartwatch
PPG signals. In this paper, we propose a novel deep learning based approach,
BayesBeat that leverages the power of Bayesian deep learning to accurately
infer AF risks from noisy PPG signals, and at the same time provide the
uncertainty estimate of the prediction. Bayesbeat is efficient, robust,
flexible, and highly scalable which makes it particularly suitable for
deployment in commercially available wearable devices. Extensive experiments on
a recently published large dataset reveal that our proposed method BayesBeat
substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure