Breathing Rate Monitoring During Sleep from a Depth Camera Under Real-Life Conditions

Abstract

Computer vision is a non-invasive way to supervise patients in the bed. We introduce a novel algorithm that monitors breathing rate from a depth camera placed above the bed. While visually registering breathing rate has raised significant interest in the health care field, most published approaches are evaluated only on constrained or simulated settings. Conversely, we evaluate our method in a real dataset consisting of 3,239 segments collected from 67 sleep laboratory patients. Our method introduces three novel contributions: a dynamic Region of Interest (RoI) which is aligned to the bed, a confidence metric based on patient agitation, and the Early Fourier Fusion strategy. Overall, our camera based method is accurate on 85.9% of the segments. This performance is similar to the obtained from a chest sensor (88.7%). Most importantly, we report the performance impact related to different sleep conditions, like apnea, position and staging.