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

Ching-Wei Wang*, A. Ahmed, A. Hunter

*Corresponding author for this work

Research output: Contribution to journalConference proceeding article (ISSN)peer-review

12 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)4469-4473
JournalProceedings of the 28th IEEE EMBS Annual International Conference
Publication statusPublished - 3 Sep 2006
Event2006 International Conference of the IEEE Engineering in Medicine and Biology Society - New York, United States
Duration: 30 Aug 20063 Sep 2006

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