||Photoplethysmographic Dynamic Errors of Fractional-Order Chaos Synchronization and Color Relational Analysis in Computer-assisted Diagnosis of Peripheral Arterial Disease
||Department of BioMedical Engineering
peripheral arterial disease
type 2 diabetes mellitus
ankle-brachial pressure index
dynamic error system
color relational analysis
support vector machine
wolf pack search
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。
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。
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.
LIST OF FIGURES IV
LIST OF TABLES V
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
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