血壓波形是重要的生理資訊，隱含著心臟血管功能的重要訊息。對於橈動脈血壓波形的檢測，醫院加護病房或手術房大都以侵入式為之，但這種作法對於患者有潛在風險，因此，本論文提出一種以非侵入式的方法，應用粒子群聚最佳化法之系統識別，以重建橈動脈血壓波形，主要目的是希望利用非侵入的方式，利用手指光學血液容積信號(Photoplethysmography, PPG)，配合粒子群聚最佳化法(Particle Swarm Optimization, PSO)及fuzzy C-means建立最佳波形轉換函數庫，使不同特徵的光學血液容積信號均有最佳轉移函數，以獲得精確的連續橈動脈血壓波形。在作法上我們採用轉移函數庫的構想，以事先的測試信號，針對不同特徵，建立專屬轉移函數。爾後的量測信號，只要比對轉移函數庫中的PPG信號，找出最佳歸類叢集(cluster)，即可以此叢集轉移函數做波形轉換，可快速及準確的獲得連續的橈動脈血壓波形。

Waveforms of blood pressure contain very important information of life. Although blood pressure can be continuously measured by an intra arterial catheter, this invasive method introduces risks to patients. Therefore, a noninvasive method in measuring blood pressure waveforms is proposed in this paper, based on which we can use the signals of fingertip photoplethysmogram to reconstruct radial pressure waveforms. Characteristics of various photoplethysmogram will be categorized into 3 clusters by using fuzzy C-mean clustering. A particle swarm optimization scheme is then established to search for an optimal transfer function model for estimating the radial pressure waveforms. When the PPG signals of various characteristics become available, an optimization scheme based on Particle Swarm Optimization (PSO) is proposed to derive a transfer function bank for reconstructing continuous radial pressure waveforms for other patients. The optimization scheme based on PSO has successfully applied to derive a set of transfer function banks with satisfactory performance in providing estimates for actual blood waveforms. Experiment results show that correlation ratio of the transformed waveforms can be as high as 0.89, much better than the results via the ARX technique.

目 錄
中 文 摘 要	Ⅱ 
英 文 摘 要	Ⅲ
致謝	Ⅳ
目錄	Ⅴ
圖目錄	Ⅵ
表目錄	Ⅸ
第一章  緒論	1
     1.1  研究背景與動機	1
1.2  研究方法	2
1.3  內容簡介	2
1.4  論文架構	3
第二章  粒子群聚最佳化演算法	5
2.1  最佳化法簡介	5
2.2  粒子群聚最佳化法(PSO)	6
第三章  血壓波形與光學血液容積信號特徵	14
3.1  血壓波形的生理意義	14
     3.2  光學血液容積信號(PPG)的特徵	16
3.3  光學血液容積信號量測方式與原理	20
第四章  血壓波形轉換	23
4.1 模型階數評估(Model order estimation)	23
4.2 模型係數評估(Model parameter estimation)	26
4.3 均方根誤差(RMSE)之評估	27
4.4 相關性比率(CORRCOEF)之評估	28
第五章  血壓波形之系統識別	29
5.1 外部自回歸法(ARX)分析建模	31
5.2 粒子群聚最佳化法分析建模	34
第六章  分析實驗結果	37
     6.1波形分類	37
     6.2數據分析	40
     6.3實驗結果	52
第七章  結論與未來研究方向	57
7.1結論	57
     7.2未來研究方向	57
參考文獻	60
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圖目錄
圖2-1 粒子群聚最佳化法流程圖………………………………………13
圖3-1 人體中不同測量點及不同年齡的血壓波型………………………15
圖3-2 血壓脈波與心電圖在時間上的關係….……………..…………….15
圖3-3 光學血液容積信號圖直流及交流成分示意圖……………………18
圖3-4 光強度變化簡易模型……………………………..………………20
圖3-5 穿透式血管管徑變化偵測示意圖……………………………........21
圖3-6 PPG連續變化波形…………………………………………........22
圖4-1 建立轉移函數流程示意圖……………………………………........23
圖5-1 系統識別基本程序…………………………………………….…..29
圖5-2 ARX 模型圖………………………………………………....……..33
圖5-3 粒子群聚最佳化法流程圖…………………………………………34
圖5-4 PSO導出轉移函數示意模型……………………………………….36
圖6-1 PPG 分類波形，其中紅線為向量中心…………………………..38
圖6-2 血壓波形與手指光學血液容積波形………………..…………..39
圖6-3 轉換血壓波形與原血壓波形之比對………………..…………..40
圖6-4 (Lab01)樣本實驗評估圖………………………………..…………..41
圖6-5 (Lab02)樣本實驗評估圖………………………………..…………..41
圖6-6 (Lab03)樣本實驗評估圖………………………………..…………..42
圖6-7 (Lab04)樣本實驗評估圖………………………………..…………..42
圖6-8 (Lab05)樣本實驗評估圖………………………………..…………..42
圖6-9 (Lab06)樣本實驗評估圖………………………………..…………..43
圖6-10 (Lab07)樣本實驗評估圖……………………………..…………..43
圖6-11 (Lab08)樣本實驗評估圖……………………………..…………..44
圖6-12 (Lab09)樣本實驗評估圖……………………………..…………..44
圖6-13 (Lab10)樣本實驗評估圖……………………………..…………..45
圖6-14 (Lab11)樣本實驗評估圖……………………………..…………..45
圖6-15 (Lab12)樣本實驗評估圖……………………………..…………..46
圖6-16 (Lab13)樣本實驗評估圖……………………………..…………..46
圖6-17 (Lab14)樣本實驗評估圖……………………………..…………..47
圖6-18 (Lab15)樣本實驗評估圖……………………………..…………..47
圖6-19 (Lab16)樣本實驗評估圖……………………………..…………..48
圖6-20 (Lab17)樣本實驗評估圖……………………………..…………..48
圖6-21 (Lab18)樣本實驗評估圖……………………………..…………..49
圖6-22 (Lab19)樣本實驗評估圖……………………………..…………..49
圖6-23 (Lab20)樣本實驗評估圖……………………………..…………..50
圖6-24 非參數化法之時域與頻域分析比較波形圖………..…………..53
圖6-25 非參數化法之頻率響應概念圖……………………..…………..54
圖6-26 參數化法之時域與頻域分析比較波形圖…………..…………..55
圖6-27 參數化法之頻率響應概念圖………………………..…………..56
=====================================================

表目錄
表3-1 動脈血壓波型及其生理意義…..………….…….…………………16
表6-1 以RMSE為適應函數之實驗數據圖……..………….…………….50
表6-2 以CORRCOEF為適應函數之實驗數據圖…….………………..51
表6-3 PSO與ARX模型表現結果之比較………….………….……….…52

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