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.

Originele taal-2Engels
TitelMedical Imaging 2023
SubtitelImaging Informatics for Healthcare, Research, and Applications
RedacteurenBrian J. Park, Hiroyuki Yoshida
Aantal pagina's7
ISBN van elektronische versie9781510660434
StatusGepubliceerd - 10 apr. 2023
EvenementSpie Medical Imaging 2023 - San Diego, Verenigde Staten van Amerika
Duur: 19 feb. 202324 feb. 2023

Publicatie series

NaamProceedings of SPIE


CongresSpie Medical Imaging 2023
Land/RegioVerenigde Staten van Amerika
StadSan Diego


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