以聲音特徵為基礎類神經網絡巴金森氏病預測模型：以某遠距醫療中心為例

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Please use this identifier to cite or link to this item: http://libir.tmu.edu.tw/handle/987654321/59705

Title:	以聲音特徵為基礎類神經網絡巴金森氏病預測模型：以某遠距醫療中心為例 Building Parkinson’s Disease Prediction Models Based on Speech Features Using Artificial Neural Network：Use a Telemedical Center as an Example
Authors:	凃文華 Tu, Wen-Hua
Contributors:	醫學資訊研究所
Keywords:	巴金森氏症;聲音;類神經網絡 voice;Artificial Neural Network;ANN
Date:	2020-07-02
Issue Date:	2020-10-07 16:03:57 (UTC+8)
Abstract:	巴金森氏症為常見神經退化性疾病，僅次於阿茲海默症。全球約有620萬人罹患巴金森氏症。在台灣每十萬人口約有147.7人罹病，巴金森氏症確診後的預期餘命約為7-14年。目前巴金森氏症無法完全治癒，發現後僅能透過服藥方式來維持病期或藉由手術來改善病情、避免急速惡化，因此，及早發現及早就醫以維持病情對患者極為重要。巴金森氏症這類慢性中樞神經系統退化的疾病，較難早期發現，但巴金森氏症患者有70%-90%患者出現語言障礙和聲音疾病，如單一聲調特徵較低、講話含糊、發音功能緩慢或是不協調的症狀。本研究透過精細聲音分析軟體擷取國內遠距醫療中心客戶日常生理狀態的聲音特徵數據，透由類神經網絡建構巴金森氏病預測模型，準確度0.951、敏感度0.958、特異度0.941，可辨別人們於有無罹患巴金森氏症的狀態，提供臨床醫療的診斷參考，並打破目前的醫療系統巴金森氏症患者須與醫生面對面才能監測診斷治療情況。
Description:	碩士指導教授:邱泓文委員:徐建業委員:葉篤學
Data Type:	thesis
Appears in Collections:	[ ] Dissertations/Theses

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