臺灣博碩士論文加值系統

衛生署公佈的國人十大死因統計顯示有七類與慢性疾病相關，近年來國人因肥胖問題大幅提高罹患代謝症候群的盛行率，而代謝症候群更是引發心血管疾病與第二型糖尿病的高風險族群。此族群通常伴隨血壓、血糖與血脂上升等異常現象，容易造成動脈血管的病變。本研究藉由建構循環功能整合評估系統，透過實驗室現有的心電圖、雷射都卜勒血流儀，與自行開發的動脈血壓波形、光容積變化描計圖等設備，運用非侵入式量測技術快速評估主要動脈血管與局部血流的循環狀態，再以生物統計分析代謝症候群對血管特性的影響程度。
藉由右手與右腳等體表部位偵測血壓與血流訊號，依受試者血管傷害程度由高至低為「糖尿病患者(Z組)」、「代謝症候群患者(A組)」、「未達標準之潛伏性代謝症候群者(B組)」、「非代謝症候群者(C組)」等四組，並運用時域、諧波分析參數評估差異。在參數分析結果可以觀察由Z組到C組存在明顯遞增或遞減趨勢，例如：BPW與LDF之時域參數多為遞增，BPW與LDF之時域參數變異率分別為遞減與持平。BPW諧波參數，在第1~3諧波皆為先下降後上升，第4~10諧波則為遞減；BPW諧波參數變異率，在第1~3諧波皆為遞減，第4~9諧波則為先下降後上升。
BPW參數可檢視身體主要動脈的血管特性變化，LDF參數則可檢視周邊血管的阻力狀況，而各參數變異率能協助評估身體調控的活動程度。由數據結果可以解釋血管受損嚴重的Z、A組，其供血阻力變化相較正常的B、C組可能更為明顯，因為血管特性或周邊阻力發生明顯改變，全身供血需要重新分配調控。
在儀器開發上，將自行研製的BPW診斷儀器實際應用於臨床健檢，目前對於代謝症候群不同階段已具備良好解析能力，還可降低設備成本與維持訊號穩定性。透過非侵入式檢測提升使用者親和度，希望未來落實遠距醫療與居家照護的目標。在預防醫學上，藉由數據變化趨勢連結糖尿病、心血管疾病等慢性疾病之關連，未來可實現慢性疾病的早期檢測、早期預防，提升臨床應用價值。

The Department of Health announced that seven classes in ten leading cause of death was related with chronic disease in Taiwan. Because obesity increased of suffering from metabolic syndrome, which was high risk group caused by cardiovascular disease and type 2 diabetes. The group was usually accompanied by high blood pressure, high blood sugar, and high blood lipid. In the late stage, it not only easily damaged to vascular but also caused arterial lesions. The aim of this study was to establish blood-flow monitoring system in the evaluation of metabolic syndrome, which integrated various measurement devices, such as electrocardiogram (ECG), laser Doppler flowmetry (LDF), and self-developed blood pressure waveform (BPW), photoplethysmography (PPG). With these non-invasive technologies, we could quickly measure circulatory conditions from main arterial properties and peripheral blood flow, and know how metabolic syndrome changed of vascular characteristics.
By the aid of measuring the blood flow and pressure signals from right hand and foot, which of the following two forms of parameters: time-domain and harmonic analysis. With discriminating the vascular lesions from four groups: the subject with diabetes was called Z group, the subject with metabolic syndrome was called A group, the subject with latent metabolic syndrome was called B group, the subject with non-metabolic syndrome was called C group. As a result, we could find that it mainly had different tendency from Z group to C group. For example, the time-domain parameters of BPW and LDF were mostly rising, the coefficient of variation parameters in time-domain of BPW and LDF were mostly falling. The 1-3 harmonic parameters of BPW had a trend of fall first then rise, and the 4-10 harmonic were falling; The coefficient of variation parameters in 1-3 harmonic of BPW were falling, and the 4-10 harmonic had a trend of fall first then rise.
BPW could help to monitor the characteristic changes in arterial vascular, LDF could monitor the resistance changes in peripheral vascular, and the coefficient of variation could evaluate the activities control in body. In instrument development, we used self-developed BPW diagnostic device in clinical. In addition to having good resolution, we could reduce facility cost and maintain signal stability. Through non-invasive technology raising user friendliness, we aimed at achieving telemedicine and home care services. In preventive medicine, by the aid of data tendency connecting with diabetes and cardiovascular diseases, realizing early detection and early prevention for chronic disease in the future, and improving clinical application.

中文摘要..................................................Ⅰ
英文摘要..................................................Ⅱ
誌謝......................................................Ⅲ
圖表索引..................................................Ⅵ

第一章 緒論
1.1 研究背景......................................1
1.2 研究動機......................................3
1.3 研究目的......................................4
1.4 非侵入式量測技術..............................5
1.5 論文架構......................................6
第二章 實驗硬體與設計
2.1 實驗設備......................................8
2.1.1 自行設計的血壓波形感測器(BPW).................8
2.1.2 血壓波形感測器的機構建置.....................11
2.1.3 雷射都卜勒血流儀(LDF)........................17
2.1.4 光容積變化描計圖(PPG)........................18
2.1.5 生理訊號放大器(ECG)..........................20
2.1.6 訊號連接器...................................21
2.1.7 類比數位擷取卡(ADC card).....................22
2.2 實驗流程.....................................23
2.2.1 實驗前置.....................................24
2.2.2 實驗進行.....................................29
2.2.3 實驗結束.....................................29
第三章 實驗方法與參數
3.1 實驗方法.....................................30
3.2 分析參數介紹.................................32
3.2.1 LDF時域分析..................................32
3.2.2 BPW時域、諧波分析............................34
第四章 實驗結果
4.1 數據結果.....................................39
4.2 BPW時域參數分析結果..........................39
4.2.1 BPW時域參數..................................39
4.2.2 BPW時域參數變異率............................40
4.3 BPW諧波參數分析結果..........................41
4.3.1 BPW諧波參數..................................41
4.3.2 BPW諧波參數變異率............................41
4.4 LDF時域參數分析結果..........................42
4.4.1 LDF時域參數..................................42
4.4.2 LDF時域參數變異率............................43
第五章 實驗討論與未來展望
5.1 參數結果.....................................44
5.1.1 各時域參數與時域參數變異率...................44
5.1.2 BPW諧波參數與諧波參數變異率..................45
5.2 數據討論.....................................45
5.2.1 BPW時域分析(表5.1、表5.2)....................46
5.2.2 BPW諧波分析(表5.1、表5.2)....................46
5.2.3 LDF時域分析(表5.1、表5.2)....................47
5.3 研究結論.....................................47
5.4 未來展望.....................................48
參考資料..................................................49

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