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研究生:賴佑政
研究生(外文):Lai, Yu-Cheng
論文名稱:用於評估用藥成效之生物回饋行動雲端平台
論文名稱(外文):A biofeedback mobile cloud platform for evaluating medication response
指導教授:張玉山張玉山引用關係
指導教授(外文):Chang, Yue-Shan
口試委員:黃有評鐘國軒戴志華張玉山
口試委員(外文):Haung, You-pingChung, Kuo-HsuanTai, Chih-HuaChang, Yue-Shan
口試日期:2017-11-01
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:78
中文關鍵詞:生物回饋用藥成效分析貝葉斯網路雲端計算
外文關鍵詞:BiofeedbackDrug use analysisBayesian NetworkCloud computing
相關次數:
  • 被引用被引用:0
  • 點閱點閱:249
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  • 下載下載:17
  • 收藏至我的研究室書目清單書目收藏:0
醫師針對憂鬱症患者經常開立憂鬱症藥物給予治療,一般而言從投藥開始至少需要6-8周的觀察來評估徵狀是否緩和,來評估此用藥是否具有療效,但往往都經過6-8周後複診才發覺是否出現過度治療(overtreatment)或是低度治療(undertreatment),進而調整用藥;如何可以快速的評估患者使用藥物治療以達到精準治療,過去的研究曾藉由腦波特徵擷取或者用電流源分析能夠預測重度憂鬱症的療效,並且在藥物治療的療效預測上有不錯的成果,在心率變異相關的研究中有人提出憂鬱症嚴重程度的值有負相關可做為憂鬱症症狀的一個參考;本論文將植基於過去對於生理與心理之資訊融合技術及平台之建置為基礎,將透過穿戴式裝置及行動裝置即時收集使用者之心律變異(HRV)、腦波感測器(EEG)之生理資料及憂鬱情緒之心理資料,建置生物回饋之資訊收集平台,透過雲端平台及樸素貝葉斯網路的方法論融合生理與心理資訊推論出融合分數,本研究模型的成果達到藥物療效評估裡,敏感度為80.0%及特異度為93.3%在ROC曲線積分面積(AUC),達到0.887具有初步可參考價值,未來隨著在醫院收集收案數的增加將會有更精準的成果。以協助醫生可以快速及正確的評估用藥情形進而決定是否調整治療方法及用藥,以便達到個人化之精準醫療(Precision Medicine)之目的。
Recently biofeedback and neurofeedback has been more popular and frequently that found insomnia, anxiety or depression and other diseases are associated with heart rate variability and brain waves closely related. Biofeedback platform utilize wearable devices to collect the physiological and psychological data of the patients with depression in order to evaluate the patient's medication effectiveness through biofeedback, so as achieve the purpose of personalized and precision medicine. In this study, we will build a biofeedback collection platform, through the cloud platform, fusion technology, design of ontology and its inference rules, and the establishment of emotional abnormal assessment model. The results of evaluating medication response is accuracy of 83.1%, sensitivity of 81.9% and specificity of 78.8%.The area under the ROC curve(AUC) for this study is 0.887. Therefore, the patient’s HRV and EEG can be instantaneously collected through wearable devices; so that patient's biofeedback information can be used to predict the effectiveness of drug use, so as to help doctors to evaluate all drug use correctly, punctually and effectively.
ABSTRACT IV
目錄 V
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 問題與動機 1
1.2 目標與貢獻 2
1.3 論文架構 4
第二章 背景與相關研究 5
2.1 憂鬱症情緒狀態與用藥情形 5
2.1.1 憂鬱症情緒狀態 5
2.1.2 憂鬱症用藥情形 7
2.2 腦波與憂鬱症的關聯性 9
2.3 心率變異分析與憂鬱症關聯 12
2.4 本體論 15
2.5 貝葉斯網路與樸素貝葉斯 16
2.5.1 貝氏定理 16
2.5.2 樸素貝葉斯網路 (Naïve bayes network) 19
2.6 ROC曲線 (Receiver Operating Characteristic Curve) 21
第三章 用藥治療評估的本體論與貝葉斯網路模型 25
3.1 本體論推論至貝葉斯網路模型 30
3.2 貝氏網路子節點特徵變數定義 31
3.3 Fusion 推論 34
第四章 系統架構與流程 36
4.1 系統架構 36
4.1.1 前端用戶模組 37
4.1.2 伺服器後端模組 40
4.2 系統流程 42
4.2.1 資料融合處理流程 43
4.2.2 使用者操作流程 44
第五章 系統實作與分析 46
5.1 受測者條件 46
5.2 實驗流程 47
5.3 實驗環境 48
5.4 系統實作 49
5.5 實驗數據分析與比較 52
5.5.1 測量結果做ROC曲線分析 52
5.5.2 生物回饋量測結果 58
5.5.3 誤差分析 60
5.5.4 腦波、心率變異、心情比較分析 60
第六章 結論與未來目標 62
參考文獻 63
附錄A 人體試驗訓練證明 68
附錄B 人體實驗通過證明函 69


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