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

Wei Wang Ching; Amr Ahmed; Andrew Hunter

doi:10.1109/IEMBS.2006.260648

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

Wei Wang Ching^*, Amr Ahmed, Andrew Hunter

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Conference proceeding article (ISSN) › peer-review

13 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 language	English
Pages (from-to)	4469-4473
Number of pages	5
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
DOIs	https://doi.org/10.1109/IEMBS.2006.260648
Publication status	Published - 3 Sept 2006
Event	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: 30 Aug 2006 → 3 Sept 2006

Keywords

Behavior recognition
Breath monitoring
Respiration monitoring
Vision analysis

Research Centres

Centre for Intelligent Visual Computing Research

Access to Document

10.1109/IEMBS.2006.260648

Cite this

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title = "Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea.",

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",

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Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea. / Ching, Wei Wang; Ahmed, Amr; Hunter, Andrew.
In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 03.09.2006, p. 4469-4473.

Research output: Contribution to journal › Conference proceeding article (ISSN) › peer-review

TY - JOUR

T1 - Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea.

AU - Ching, Wei Wang

AU - Ahmed, Amr

AU - Hunter, Andrew

PY - 2006/9/3

Y1 - 2006/9/3

N2 - 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

AB - 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

KW - Behavior recognition

KW - Breath monitoring

KW - Respiration monitoring

KW - Vision analysis

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DO - 10.1109/IEMBS.2006.260648

M3 - Conference proceeding article (ISSN)

SN - 0589-1019

SP - 4469

EP - 4473

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

Y2 - 30 August 2006 through 3 September 2006

ER -

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

Abstract

Keywords

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