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Abstract

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.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsBrian J. Park, Hiroyuki Yoshida
PublisherSPIE
Number of pages7
ISBN (Electronic)9781510660434
DOIs
Publication statusPublished - 10 Apr 2023
EventSpie Medical Imaging 2023 - San Diego, United States
Duration: 19 Feb 202324 Feb 2023

Publication series

NameProceedings of SPIE
Volume12469

Conference

ConferenceSpie Medical Imaging 2023
Country/TerritoryUnited States
CitySan Diego
Period19/02/2324/02/23

Keywords

  • AI
  • atrial fibrillation
  • camera
  • optical flow
  • remote PPG
  • respiration
  • RGB

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