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Electrocardiogram Signal for the Detection of Obstructive Sleep Apnoea Via Artificial Neural Networks

SAS has become an increasingly important public-health problem in recent years. It can adversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Moreover, up to 90% of these cases are obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and treat OSA is becoming a significant issue, both academically and medically. Polysomnography can monitor the OSA with relatively fewer invasive techniques. However, polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. Therefore, to improve such inconveniences, one needs to develop a simplified method to diagnose the OSA, so that the OSA can be detected with less time and reduced financial costs.
Since currently there seems to be no OSA detection technique available in Taiwan, the goal of this work is to develop a reliable OSA diagnostic algorithm. In particular, via signal processing, feature extraction and artificial intelligence, this thesis describes an on-line ECG-based OSA diagnostic system. It is hoped that with such a system the OSA can be detected efficiently and accurately.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0701104-182302
Date01 July 2004
CreatorsWang, Yuan-Hung
ContributorsPei-Jung Chen, Jian-De Li, Cheng-Wen Yan
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0701104-182302
Rightsunrestricted, Copyright information available at source archive

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