4.6 Article

Unconstrained Video Monitoring of Breathing Behavior and Application to Diagnosis of Sleep Apnea

期刊

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 61, 期 2, 页码 396-404

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2013.2280132

关键词

Action recognition; behavior analysis; breathing monitoring; obstructive sleep apnea (OSA)

资金

  1. United Lincolnshire Hospitals NHS Trust
  2. University of Lincoln

向作者/读者索取更多资源

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e. g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing).

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