Smartwatch Photoplethysmogram-Based Atrial Fibrillation Detection with Premature Atrial and Ventricular Contraction Differentiation Using Densely Connected Convolutional Neural Networks

This study addresses the challenges of arrhythmia detection, including atrial fibrillation (AF), using continuously collected smartwatch photoplethysmography (PPG) data. We propose a novel application of the densely connected convolutional neural network (DenseNet) for AF detection using smartwatch PPG data with effective differentiation from premature atrial contractions (PACs) and premature ventricular contractions (PVCs). While smartwatches offer a convenient platform for continuous monitoring, current research often relies on controlled settings using fingertip PPG and struggles to differentiate AF from other arrhythmias, particularly PAC/PVC <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex>. Unlike prior controlled-setting studies, our data collection includes 14 days of continuous smartwatch PPG data in real-world settings, which truly challenges the performance of arrhythmia classification algorithms. Notably, the proposed DenseNet model excelled at distinguishing AF (sensitivity: 0.950, specificity: 0.949), demonstrating promise for real-world continuous AF monitoring. The proposed DenseNet model also achieved high specificity (0.958) for the classification of PAC/PVCs despite the inherent difficulty in their identification from PPG data. These findings demonstrate the efficacy of DenseNet in accurately identifying AF and distinguishing it from other arrhythmias, highlighting its potential for widespread clinical application.