Early Detection and Burden Estimation of Atrial Fibrillation in Ambulatory Free-living Environment

Early detection and accurate burden estimation of AFib can provide the foundation for effective physician treatment and attract tremendous attention in recent years. In this paper, we develop a novel smartwatch-based system to achieve detection of AFib episodes and estimation of AFib burden in ambulatory free-living environment withour user engagement. Our system leverages built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes
time-frequency (TF) analysis to augment features in both time and frequency domain. Finally, a super lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we collaborate with medical professionals and carry out a clinical study to enroll 53 participants across 3 months. For each participant, we collect and annotate more than 336 hours of data. Our systems can achieve average 91.6% accuracy, 0.930 specificity, and 0.908 sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system has the potential to provide the clinical assessment of AFib in daily living.