Respiration Extraction and Atrial Fibrillation Detection From Clinical Data Based on Single RGB Camera

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.