Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea.
Recognizing abnormal breathing activity from body movement is a challenging task in machine vision. In this paper, we present a non-intrusive automatic video monitoring technique for detecting abnormal breathing activities and assisting in diagnosis of obstructive sleep apnoea. The proposed technique utilizes infrared video information and avoids imposing geometric or positional constraints on the patient. The technique also deals with fully or partially obscured patients' body. A continuously updated breathing activity template is built for distinguishing general body movement from breathing behavior.
Wang
Ching Wei
CW
Dept. of Comput. & Informatics, Univ. of Lincoln, UK. cweiwang@lincoln.ac.uk
Ahmed
Amr
A
Hunter
Andrew
A
D000465:Algorithms; D045422:Continuous Positive Airway Pressure; D004867:Equipment Design; D006801:Humans; D007259:Infrared Rays; D015233:Models, Statistical; D009058:Mouth Breathing; D009068:Movement; D011175:Positive-Pressure Respiration; D011187:Posture; D012119:Respiration; D012890:Sleep; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000473:pathology*; D014741:Video Recording; D014785:Vision, Ocular