Abstract:

Early detection and diagnosis of atrial fibrillation is the key to reducing the risk of atrial fibrillation and complications. Although the video photoplethysmography (VPPG) technology provides a new approach to atrial fibrillation screening, it is susceptible to motion interference in real-world scenarios. When the existing VPPG atrial fibrillation detection method encounters motion interference, the pulse signal will be distorted, resulting in misjudgment. To solve the above problems, an anti-interference video atrial fibrillation detection model was proposed. The model employed an attention encoder network to extract robust spiking latent features from spiking signals containing motion disturbances. A radial basis classification network then performed atrial fibrillation detection based on these latent features. The attention encoder mapped complex impulse signals into high-dimensional subspaces, focusing on effective information and extracting robust latent features. Furthermore, Radial Basis Classification Network enhanced atrial fibrillation recognition ability under the supervision of atrial fibrillation labels and output reliable results. Experiments were carried out on a self-built dataset with 200 testers, and the results show that the proposed model performed well in various scenarios. In static scenes, the detection accuracy was 8.1% higher than the optimal comparison algorithm, and the sensitivity was 7.5% higher. In dynamic scenes, where the accuracy of the comparison algorithms was greatly reduced, the accuracy of the proposed model was improved by 16.5%, and the specificity was improved by 18.3%. The model demonstrated good anti-motion interference ability, effectively eliminating the influence of motion interference and improving the detection accuracy of video atrial fibrillation in real scenes.

Key words: video photoplethysmography, atrial fibrillation detection, anti-motion interference, attention encoder, potential characteristics