| Summary: | 碩士 === 中原大學 === 通訊工程碩士學位學程 === 102 === The sleep quality index of traditional sleep quality assessment is lack of digital data although the results could be obtained quickly. Polysomnography (PSG) could obtain sufficient clinical data but it is high cost, time consuming and uncomfortable for patients that discourages the patients for continuing examinations and treatment.
In this study, a speech signal processing method is applied to obtain characteristic parameters from time and frequency domain analysis that combines “speech signal processing” and “sleep quality assessment” to provide the sleep quality index a quantitative data in one minute, from which the Obstructive-Sleep Apnea (OSA) patients could be diagnosed. The speech signals from normal and from OSA patients before and after treatment are compared and the results show the most stable signals in area energy are from the normal and the signal stability from OSA ones after treatment is between the normal and the OSA ones before treatment. This shows that the signal stability of OSA patients could be similar to the stability of the normal after treatment.
Finally, Fisher’s Linear Discriminant is applied to draw an identification curve to classify the normal and the OSA patients before treatment, of which identification rate for the normal reaches 100% and for the OSA ones before and after treatments reaches 91.7%. It is expected that the results could serve as support for doctors to use current sleep quality index for rapid examinations and treatment effect evaluation and serve as a simple self-assessment reference for patients that is beneficial for people in need and for medical development.
|
|---|
