Artificial intelligent vision analysis in obstructive sleep apnoea (OSA)

Wang, Ching Wei and Ahmed, Amr and Hunter, Andrew (2006) Artificial intelligent vision analysis in obstructive sleep apnoea (OSA). In: 30th Anniversary Conference of the Association for Respiratory Technology and Physiology (ARTP) 2006, 26 - 27 Jan 2006, Brighton, UK.

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Abstract

Although polysomnography is a generally adopted approach for diagnosing obstructive sleep apnoea (OSA), there are several critical drawbacks with it, including massive equipment cost, large expense on replacing damaged components and more importantly invasive devices required to be worn while patients are struggling to sleep. Furthermore, there is no proof that polymonography obtains higher accuracy in detecting patients with OSA than more simple investigations [1]. Video monitoring has been adopted to assist diagnosis on obstructive sleep apnoea. From practical researches [3], the best predictors of morbidity in individual patients, as assessed by improvements with CPAP therapy, are nocturnal oxygen saturation [4, 5] and movement during sleep [4]. Hence, we purpose a robotic, objective and reliable video monitoring system with AI intelligence for analysis on human behavior during sleep, automatically generating a statistics report on body activity, including arm movement, limb movement, head movement and body rotation movement and arousal movement.

Item Type:Conference or Workshop Item (Poster)
Additional Information:Although polysomnography is a generally adopted approach for diagnosing obstructive sleep apnoea (OSA), there are several critical drawbacks with it, including massive equipment cost, large expense on replacing damaged components and more importantly invasive devices required to be worn while patients are struggling to sleep. Furthermore, there is no proof that polymonography obtains higher accuracy in detecting patients with OSA than more simple investigations [1]. Video monitoring has been adopted to assist diagnosis on obstructive sleep apnoea. From practical researches [3], the best predictors of morbidity in individual patients, as assessed by improvements with CPAP therapy, are nocturnal oxygen saturation [4, 5] and movement during sleep [4]. Hence, we purpose a robotic, objective and reliable video monitoring system with AI intelligence for analysis on human behavior during sleep, automatically generating a statistics report on body activity, including arm movement, limb movement, head movement and body rotation movement and arousal movement.
Keywords:vision analysis, obstructive sleep apnoea
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:112
Deposited By: Bev Jones
Deposited On:27 Sep 2006
Last Modified:13 Mar 2013 08:22

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