Title page for etd-0701104-182302


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URN etd-0701104-182302
Author Yuan-Hung Wang
Author's Email Address m9138621@student.nsysu.edu.tw
Statistics This thesis had been viewed 5361 times. Download 7687 times.
Department Mechanical and Electro-Mechanical Engineering
Year 2003
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Electrocardiogram Signal for the Detection of
Obstructive Sleep Apnoea Via Artificial Neural Networks
Date of Defense 2004-06-21
Page Count 96
Keyword
  • OSA
  • ECG
  • Neural Network
  • Abstract 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.
    Advisory Committee
  • Jian-De Li - chair
  • Pei-Jung Chen - co-chair
  • Cheng-Wen Yan - advisor
  • Files
  • etd-0701104-182302.pdf
  • indicate access worldwide
    Date of Submission 2004-07-01

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