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Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea

By Ching-Wei Wang, Amr Ahmed and Andrew Hunter

Abstract

Recognizing abnormal breathing activity from\ud 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\ud for distinguishing general body movement from breathing\ud behavior

Topics: G400 Computer Science
Publisher: Institute of Electrical and Electronics Engineers, Inc
Year: 2006
OAI identifier: oai:eprints.lincoln.ac.uk:114

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