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系統識別號 U0026-2701201410514100
論文名稱(中文) 光體積動態誤差的分數階混沌同步化和彩色關聯性分析法之電腦輔助診斷週邊動脈病
論文名稱(英文) Photoplethysmographic Dynamic Errors of Fractional-Order Chaos Synchronization and Color Relational Analysis in Computer-assisted Diagnosis of Peripheral Arterial Disease
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 102
學期 1
出版年 103
研究生(中文) 李健明
研究生(英文) Chien-Ming Li
學號 P88931084
學位類別 博士
語文別 英文
論文頁數 95頁
口試委員 指導教授-陳天送
口試委員-陳培展
口試委員-林家宏
口試委員-蔣依吾
口試委員-陳永福
口試委員-陳家進
中文關鍵字 週邊血管病  糖尿病  踝肱血壓指數  光體積描計法  分數階微積分  混沌同步化  動態誤差  模式辨識  彩色關係分析  支持向量機  狼群搜索演算法 
英文關鍵字 peripheral arterial disease  type 2 diabetes mellitus  ankle-brachial pressure index  photoplethysmography  dynamic error system  fractional-order calculus  chaos synchronization  color relational analysis  support vector machine  wolf pack search 
學科別分類
中文摘要 下肢週邊血管疾病 (週血管病;PAD) 是一種很普遍但又隱密不易診斷的疾病,主要是由動脈粥樣硬化血管內壁所造成,導致動脈閉塞而下肢壞疽。週血管病最主要的危險因子是第二型糖尿病,老化,和吸煙,但是其他包括高血壓、高膽固醇、和高同型半胱氨酸血症等也會增加週血管病的風險。
大部分病人和醫師都不太了解週血管病,因此延遲診斷和治療並非少見。這種疾病的典型臨床表現為間歇性跛行和休息時的腿痛,但有的是以糖尿病足、感染蜂窩性組織炎、或骨髓炎為最初的臨床表現。無論何種表徵,週邊血管觸診異常否,動脈阻塞程度仍無法精確得知;唯有依據傳統的、電腦斷層、或磁振造影的血管攝影術方知下肢的那些血管阻塞。但在臨床表現之前,即使是有上述危險因子的,也無從了解自己是否罹患週血管病。延遲診斷的原因之一其是缺乏早期診斷的篩檢和診斷工具,當然也就無法積極監測週血管病的發展和治療的效果。在區域醫院和醫學中心因為具有血管攝影的設備,晚期和危及下肢的週血管病可以使用經皮血管形成術疏通阻塞。
但是在無症狀期,目前只有杜普勒超音波和踝肱動脈血壓指數可以診斷週血管病。踝肱脈壓指數的弱點是操作不易、本質上易生誤差、和無法使用於相當程度硬化的血管;尤其是最後一項,更是他的致命傷,因為許多年長者、尿毒症和高血壓患者的下肢動脈是有嚴重的鈣化。即便如此,當前國際上的週血管病診斷參考標準,仍然是踝肱脈壓指數。這個方法把週血管病的嚴重程度分為正常、低度、和高度三種,根據各自的指數為 ≧ 0.9、(0.9, 0.5]、和 < 0.5。
光體積描計法 (photoplethysmography; PPG) 是一種光學的、非侵入性的技術,可以量測人體組織的血容量變化。目前臨床上被廣泛地應用於測量周邊血氧的飽和度合和心臟律動的相關參數。研究報告顯示週血管病患者因為長期的左右腳之間不等速的阻塞,造成兩腳之間的光體積波之傳輸時間差比健康人統計上有明顯的增長,非常具有診斷的價值。然而當代醫學仍舊缺乏診斷週血管病的光體積描計器。
這篇論文的首要目的是開發電腦輔助的診斷儀器以幫助週血管病的早期診斷和追蹤評估。第一個試驗是利用光體積描計器結合分數階混沌同步所建構的動態誤差和分類技術,紀錄25個健康的成年人和有週血管病的糖尿病人的光體積描計信號。這11名健康成年人和14例糖尿病患者,依據踝肱血壓指數區分週血管病的嚴重程度為正常、低度、和高度三種。參考心電圖R波計算 △PTTPp、△PTTf和△RT三種參數,並應用陳李氏混沌系統計算其分數階動態誤差。之後再應用彩色模型(Hue-Saturation-Value) 進行彩色關係分析 (color relational analysis),精準有效地的執行這三種週血管病嚴重度的分類。
在第二個試驗中,總共紀錄15名健康人及17例糖尿病而且有週血管病的患者進行光體積描計信號。這次的分類採用支持向量機 (support vector machine) 作為分類器,並且進行狼群搜索 (wolf pack search) 最佳化以取得必要的參數。也是精確有效地完成分類。比較人工神經網絡和其他機器學習技術,這兩種方法需要的參數、訓練時間、訓練資訊量、和統計複雜性都比較少,而且參數的調整也比較不會落入區域性極值的圈套。因此,發展一套以光體積描計法為基礎的週血管病電腦輔助診斷系統是非常具有潛力的。
光體積描計法為基礎的週血管病電腦輔助診斷系統結合彩色關係分析法和支持向量機分類技術是有可能幫助早期診斷和病程追蹤週血管病的。這樣的系統不必添加心電圖,方便攜帶,又具有即時性和準確性的優點。總之,光體積描計法的訊號經由分數階微分和陳李氏混沌同步化而建構成的動態誤差系統,再結合機器學習理論的技術如結合彩色關係分析法和支持向量機分類技術,極具發展診斷週血管病輔助工具的潛在價值,尤其是在社區、診所、和地區醫院篩選和追蹤糖尿病等高危險群,以達有效地監控週邊血管病和減少截肢的目標。
英文摘要 Lowe-limb peripheral arterial disease (PAD) is a prevalent and insidious disease caused by atherosclerosis, potentially leading to total occlusion of artery and gangrene of leg. The most common risk factors for this limb-threatening disease are type 2 diabetes mellitus, ageing, and cigarette smoking, but the pathogenesis of atherosclerosis may include hypertension, dyslipidemia, and homocysteinemia.
Underdiagnosis and under-treatment of PAD are partly because of its stealthy progress and lack of screening device for early diagnosis and active monitoring. Typical clinical manifestations of this vascular disorder are intermittent claudication and leg pain with unknown degree of arterial obstruction. In tertiary hospitals PAD can be accurately diagnosed with invasive angiography and ankle-brachial pressure index (ABI), currently deemed as a standard for evaluating severity. The severity is generally divided into normal (ABI > 0.9), low-grade (ABI = 0.9-0.5), and high-grade (ABI > 0.9) based on the ABI. Unfortunately, it is not sensitive enough to be used for prognostic evaluation of patients with hardened arteries commonly observed in diabetic and uremic.
Photoplethysmography (PPG) is an optic technique based on the association of blood volume change with light-tissue interaction. In current clinical setting, measurement of peripheral oxygenation and cardiac rhythm highly rely on this noninvasive technique. Previous studies have reported that the differences of pulse transit time (PTT) of photoplethysmograms between the right and left feet are significantly greater in PAD patients than those in healthy persons. However, a PPG-based assistant device for the diagnosis of PAD is still lacking.
The objective of this thesis is to develop a computer-assisted tool as an assistant tool for PAD diagnosis. The prototype was designed by combining dynamic error system of fractional-order chaos synchronization and pattern recognition techniques to test its potential in clinical applications. In the first experiment, photoplethysmographic signals were captured from 25 recruited subjects, including 11 healthy adults and 14 diabetic patients with three degrees of disease severity. Taking R wave of electrocardiogram as the reference, parameters △PTTp, △PTTf and △RT are determined from big toes of two feet. The results show that the dynamic errors of the fractional-order chaos synchronization and color relational analysis are able to effectively classify 3 different degrees of PAD severity. In the second experiment, 15 healthy persons and 17 diabetic patients were recruited for PPG measurement. The classification of PAD severity is also effectively conducted using support vector machine to design the classifier with wolf pack search algorithm adopted for obtaining optimal model parameters. Compared to the artificial neural network and other machine learning techniques, the proposed method is demonstrated to be efficient and effective to estimate the PAD severity.
Photoplethysmography combined with proper classification techniques consisting of color relational analysis and support vector machine is potential to design a portable computer-assisted tool for real-time PAD diagnosis. Its advantages include only small sample data are required for model training; patterns are expandable; signal capture and model training are efficient; parameter assignment is not necessary; and trapped in local minima can be avoided. Furthermore, electrocardiogram may be neglected for a PPG to be analyzed with dynamic error system of fractional-order chaos synchronization. In conclusion, PPG analyzed with color relation and modeled with support vector machine is potential in designing an assistant tool to screen and monitor the PAD in communities and hospitals.
論文目次 ACKNOWLEDGMENTS I
CONTENTS II
LIST OF FIGURES IV
LIST OF TABLES V
ABBREVIATIONS VI
ABSTRACT VIII
中文摘要 X
CHAPTER 1 INTRODUCTION 1
1.1 topic of this research 8
1.2. The goals of this research 8
1.3. Engineering design and product development 8
1.4. Contributions of this research 9
CHAPTER 2 METHODOLOGY AND TECHNIQUE 11
2.1. Introduction to peripheral arterial disease 11
2.2. Photoplethysmography 13
2.2.1. Basic principles of photoplethysmography 14
2.2.2. History of photoplethysmography 15
2.2.3. Instrumentation of photoplethysmography 17
2.2.4. Analysis of photoplethysmographic waveform 18
2.2.5. Clinical applications of photoplethysmography 19
2.3. Pattern recognition 21
2.3.1. Introduction to pattern recognition 21
2.3.2. Support vector machine 23
2.3.3. Color relational analysis 26
2.4. Fractional order derivatives 32
2.4.1. Introduction to fractional order derivatives 32
2.4.2. Principle of fractional order calculus 33
2.4.3. Applications of fractional order calculus 35
2.5. Chaos synchronization 35
2.5.1. Introduction to chaos synchronization 36
2.5.2. Basic principle of chaos synchronization 37
2.5.3. Applications of chaos synchronization 38
CHAPTER 3 EXPERIMENT AND INSTRUMENTATION 40
3.1. Background 42
3.2. Objective 44
3.3. Design of experiments 44
3.3.1. Photoplethysmography instrumentation 44
3.3.2. Photoplethysmographic signals 45
3.3.3. Fractional-order chaos synchronization of PPG signals 46
3.3.4. Color relational analysis 53
3.3.5. Support vector machine implementation 56
3.3.6. Wolf Pack Search 58
CHAPTER 4 METHODS AND PATIENTS 62
CHAPTR 5 RESULTS 65
5.1 Fractional-order chaos synchronization and color relational analysis 65
5.2 Support vector machine and wolf pack search 70
5.3 Performance comparison 75
CHAPTER 6 DISCUSSION 80
6.1 Summary 81
6.2 Further perspective 82
6.3 Conclusion 83
BIBLIOGRAPHY 86
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