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. Since 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, academically and medically. Polysomnography (PSG) can monitor the OSA with relatively fewer invasive techniques. However, PSG-based sleep studies are expansive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel.
This work develops a flow rate based detection method for apneas. In particular, via signal processing, feature extraction and neural network, this thesis introduces a flow rate based detective system. The goal is to detect OSA with less time and reduced financial costs.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0716107-155749 |
| Date | 16 July 2007 |
| Creators | Chen, Yu-Chou |
| Contributors | Lung-Wen Hang, Chen-Wen Yen, Gou-Jen Wang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716107-155749 |
| Rights | unrestricted, Copyright information available at source archive |
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