臺灣博碩士論文加值系統

根據世界衛生組織2015年統計，全球每年約有 1750 萬人死於心血管疾病，占全球死亡總數的31%。相較於其他病症，心血管疾病較無明顯的病徵，難以受一 般民眾察覺，是不可輕忽的重大疾病[4]。
如何診斷、預防心血管疾病，早期發現，早期治療，被視為是一項重要的目標，而血壓在臨床上被視為一項重要的指標。現行的血壓量測可分為侵入式與非侵入式，侵入式血壓量測需要相關的醫療設備和具備醫療知識的護理人員才可進行，而非侵入式血壓量測雖可在家中自行測量，然而其方便性卻不高，導致一般民眾的測量意願不高。
本論文探討的課題便是如何進行非侵入式的血壓預測。使用者透過穿戴式裝置，可在生活中隨時隨地的進行血壓量測，透過MIMIC資料庫所提供的數據，經由類神經網路訓練，得到血壓模型，最後經過校正模型的修正，可以使估算出的血壓逼近真實血壓。

致謝詞i
中文摘要ii
英文摘要iii
目錄iv
圖目錄vi
表目錄vii
一、 緒論 1
1.1. 研究背景 1
1.2. 研究動機與目的 2
1.3. 論文架構 3
二、相關研究 4
2.1. 心電圖 4
2.1.1. 心電圖介紹 4
2.1.2. 心電圖特徵 5
2.2. 光體積變化描記圖 6
2.2.1. PPG 介紹 6
2.2.2. PPG特徵及擷取方法 6
2.3. 臨床醫學血壓量測現況 8
2.3.1. 非侵入式血壓量測原理 8
2.3.2. 侵入式血壓量測原理 8
2.3.3. 血壓推估方法 9
三、 血壓推估方法 11
3.1. 光體積變化描記圖特徵偵測 11
3.1.1. 光體積變化描記圖(PPG) 11
3.1.2. 心臟週期(Cardiac Cycle) 13
3.1.3. PPG特徵的擷取 15
3.2. 學習網路 18
3.2.1. 倒傳遞類神經網路(BPN) 19
3.2.2. 學習流程 21
3.3. 血壓量測模型 27
四、 實驗結果 29
4.1. PPG特徵偵測驗證 29
4.1.1. 主波與次波偵測 29
4.1.2. STTS與STTD偵測 30
4.1.3. Dichrotic notch偵測 31
4.2. 血壓推估實驗 31
4.2.1. 實驗樣本 31
4.2.2. 網路架構 32
4.2.3. 血壓量測結果 33
4.2.4. 校正與學習率歸納 34
4.2.5. 多網路學習架構 38
五、結論與未來展望 41
5.1. 結論 41
5.2. 未來展望 41
參考文獻 43

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