Video health monitoring for cardiac arrhythmia detection in a real hospital scenario

Remote Photoplethysmography (remote PPG) enables contactless monitoring of the cardiac rhythm using video cameras. Prior research has shown the feasibility of video-based atrial fibrillation (AF) and/or flutter (Aflutter) detection in some scenarios, but most exclude patient movement. In this work, we investigate the feasibility of detecting these two cardiac arrhythmias in a regular hospital environment using an RGB camera, where patients were not limited in movement during the recording process. Data of 56 patients was collected before and after a scheduled cardioversion treatment. Using the data and machine learning models, we developed three models: First, a model to detect only AF from the data excluding any Aflutter cases. Here we report a sensitivity of 94.5% and a specificity of 89.3% with an AUC of 0.966. Second, a model to classify if a cardiac arrhythmia (AF or Aflutter) is present or not. There we report there a sensitivity of 95.6% and a specificity of 91.2% with an AUC of 0.975. Finally, we develop a multi rhythm model, where we classify the data in AF, Aflutter and sinus rhythm separately. The performance of arrhythmia detection is close to the second model, but we note that the distinction between AF and Aflutter is still a challenge. Here we theorize that remote PPG is more sensitive to noise during Aflutter, which will lead to features in Aflutter which are closer to those of AF. To confirm this, we will extensively review the reason of misclassification of Aflutter as AF in future work.