Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography

Atrial fibrillation (AF) has been proven highly correlated to stroke; more than 43 million people suffer from AF worldwide. However, most of these patients are unaware of their disease. There is no convenient tool by which to conduct a comprehensive screening to identify asymptomatic AF patients. Hence, we provide a non-contact AF detection approach based on remote photoplethysmography (rPPG). We address motion disturbance, the most challenging issue in rPPG technology, with the NR-Net, ATT-Net, and SQ-Mask modules. NR-Net is designed to eliminate motion noise with a CNN model, and ATT-Net and SQ-Mask utilize channel-wise and temporal attention to reduce the influence of poor signal segments. Moreover, we present an AF dataset collected from hospital wards which contains 452 subjects (mean age, 69.3$\pm$13.0 years; women, 46%) and 7,306 30-second segments to verify the proposed algorithm. To our best knowledge, this dataset has the most participants and covers the full age range of possible AF patients. The proposed method yields accuracy, sensitivity, and specificity of 95.69%, 96.76%, and 94.33%, respectively, when discriminating AF from normal sinus rhythm. More than previous studies, other arrhythmias are also taken into consideration, leading to a further investigation of AF vs. Non-AF and AF vs. Other scenarios. For the three scenarios, the proposed approach outperforms the benchmark algorithms. Additionally, the accuracy of the slight motion data improves to 95.82%, 92.39%, and 89.18% for the three scenarios, respectively, while that of full motion data increases by over 3%.