Wang, Ching-Wei and Ahmed, Amr and Hunter, Andrew (2006) Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea. In: Proceedings of the 28th IEEE EMBS Annual International Conference. Institute of Electrical and Electronics Engineers, Inc, New York, USA, pp. 4469-4473. ISBN 1424400333

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Abstract

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.

Item Type:	Book Section
Additional Information:	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.
Keywords:	breath monitoring, behavior recognition, vision analysis, respiration monitoring
Subjects:	G Mathematical and Computer Sciences > G400 Computer Science
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Bev Jones
Date Deposited:	27 Nov 2006
Last Modified:	13 Mar 2013 08:22
URI:	http://eprints.lincoln.ac.uk/id/eprint/114

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