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.

In this work, we investigated the feasibility of extracting continuous respiratory parameters from a single RGB camera stationed in a short-stay ward. Based on the extracted respiration parameters, we further investigated the feasibility of using respiratory features to aid in the detection of atrial fibrillation (AF). To extract respiration, we implemented two algorithms: chest optical flow (COF) and energy variance maximization (EVM). We used COF to extract respiration from the patient’s thoracic area and EVM from the patient’s facial area. Using capnography as the reference, for average breath-to-breath rate estimation (i.e., 15-second sliding windows with 50% overlap), we achieved errors within 3 breaths per minute with COF and within 3.5 breaths per minute with EVM. To detect the presence of AF in the respiratory signal, we extracted three respiratory features from the derived COF measurements. We fed these features to a logistic regression model and achieved an average AUC value of 0.64. This result showcases the potential of using camera-based respiratory parameters as predictors for AF, or as surrogate predictors when there is no sufficient facial area in the camera’s field of view for the extraction of cardiac measurements.