動脈硬化程度常被用於人體心血管危險評估。在本博士論文研究著重在研究手指體積波信號 (光電容積描記信號, PPG) 與多尺度龐加萊分析 (multiscale Poincaré, MSP) 方法應用。整體而言, 本博士論文研究共分兩大部分。第一部分: 研究採集34位非糖尿 病性患者 (Cluster 1) 與30位2型糖尿病性患者(Cluster 2) PPG信號, 分別採用100, 250, 500, 1000筆資料 並計算三類指標 (自主神經功能指標, SSR; 多尺度熵, MSE; 多尺度龐加萊熵, MSPI )以便 比較。SSR 與MSPI兩項指標都與空腹血糖、糖化血色素都存在具統計意義負相關。但僅有MSPI單項指標與脈壓差存在具統計意義負相關; 並且在少量資料情形下(250 或 500筆)尚能存在組群統計意義差異 (p < 0.01) 。再者, MSPI單項指標在1000筆資料情形下表現比MSE 好; 各個尺度(如, 1–10)皆能存在組群統計意義差異。第二部分研究: 研究採集10位非吸 菸年輕男人(Cluster 1)與10位吸菸年輕男人(Cluster 2)PPG信號, 兩組無年齡、身高、體重、 血壓、心率之統計差異 (皆p < 0.05)。最終SSR存在組群統計意義差異 (p = 0.015)。本博士論文研究結論: 多尺度龐加萊分析方法可有效應用在光電容積描記信號評估動脈僵硬度。

The degree of atherosclerosis is often used in the assessment of cardiovascular risk in humans. In this doctoral dissertation, the research focuses on the application of finger plethysmographic signal (PPG) and multiscale Poincaré analysis (MSP). Overall, this doctoral dissertation research is divided into two parts. The first part: The study collected PPG signals from 34 non-diabetic patients (Cluster 1) and 30 patients with type 2 diabetes (Cluster 2), using 100, 250, 500, 1000 data points and calculating three types of indicators (autonomic nervous function indicators, SSR; Multiscale Entropy, MSE; Multiscale Poincaré Entropy, MSPI) for comparison. Both SSR and MSPI had statistically significant negative correlations with fasting blood glucose and glycosylated hemoglobin. However, only the MSPI single index had a statistically significant negative correlation with the pulse pressure difference; and in the case of a small amount of data (250 or 500 records), there was still a statistically significant difference between the groups (p < 0.01). Furthermore, the single index of MSPI performs better than MSE in the case of 1000 data; each scale (eg, 1–10) can have group statistical significance differences. The second part of the study: The study collected PPG signals from 10 young non-smoking men (Cluster 1) and 10 young smoking men (Cluster 2). There were no statistical differences in age, height, weight, blood pressure, and heart rate between the two groups (all p < 0.05). There was a group statistically significant difference in final SSR (p = 0.015). Conclusions of this doctoral dissertation: The multiscale Poincaré analysis method can be effectively applied to assess arterial stiffness from photoplethysmographic signals.

Abstract..... iv
Contents .... vi
List of Tables ..... ix
List of Figures ...... x
List of Abbreviations and Symbols ... xi
List of Publications .. xii

Chapter 1. Introduction . . 1
1.1. Sample Entropy and Multiscale Entropy (MSE) . 3
1.1.1. Sample Entropy ... 3
1.1.2. Multiscale Entropy (MSE) ... 4
1.2. Poincaré Plot ... 6
1.3. Prior Work ... 7
1.4. Dissertation Outline .. 7
Chapter 2. Materials and Methods .. 9
2.1. PPG Signals Collection .. ... 9
2.1.1. The PPG System ........ 9
2.1.2. Fingertip PPG Amplitudes Calculation ..... 11
2.2. Study Populations for Two Studies ..... 13
2.2.1. Diabetic and Non-Diabetic Subjects in Study Part One..... 13
2.2.2. Smoker and Non-smoker Subjects in Study Part Two .... 13
2.3. Data Analysis Method ... 14
2.3.1. Poincaré Plot Method .. 14
2.3.2. Multiscale Poincaré (MSP) . 15
2.4. Computation Times ......... 16
2.4.1. Computation Time in Study Part One.. 16
2.4.2. Computation Time in Study Part Two.. 16
2.5. Statistical Analysis 16
2.5.1. Statistical Analysis Used in Study Part One .... 16
2.5.2. Statistical Analysis Used in Study Part Two ... 17
Chapter 3. Results 18
3.1. Poincaré Plot and MSP to Analyse PPG Signals in Diabetic and Non-Diabetic Subjects (Study Part One) 18
3.1.1. Poincaré Plot of PPG Signals to Assess Diabetes Status .. 18
3.1.2. MSP of PPG Signals for Appraising of Diabetes...... 22
3.1.3. Application of MSP vs MSE to Analyse PPG Signals in Diabetic and Non-Diabetic Subjects 24
3.1.3.1. Characteristics of Diabetic and Non-diabetic Subjects 24
3.1.3.2. Evaluation of Computational Parameters between Diabetic and Non-Diabetic Subjects . 26
3.1.3.3. Correlation of Four Different Methods with Medical and Computational Parameters ... 26
3.1.3.4. MSEaverage Fluctuations in Different Scale Factors in Diabetic and Non-Diabetic Subjects .. 28
3.1.3.5. Impact of Data Length for MSPI on Sensitivity of Distinction between Two Clusters (Diabetic and Non-Diabetic) .... 29
3.1.3.6. The Quantity of Data Points Influences the Computational Parameters in Persons with and without Diabetes 30
3.1.3.7. Time Consumption for MSEaverage and MSPI in Individuals with and without Diabetes . 30
3.2. Poincaré Plots to Analyse PPG Signals between Smokers and Non-smokers (Study Part Two) 32
3.2.1. Smoker and Non-smoker Features ...... 32
3.2.2. Evaluation of Poincaré plots in Smoker and Non-smoker . 33
3.2.3. Evaluation of the Three Parameters of the Poincaré Plot in Smokers and Non-Smokers 34
Chapter 4. Discussions ......... 35
4.1. Comparison of PPGA in Diabetic and Non-Diabetic Subjects Using the Poincaré Plot and MSP (Study Part One) .. 35
4.1.1. Poincaré plot in Diabetic and Non-Diabetic Subjects .... 35
4.1.2. MSP in Diabetic and Non-Diabetic Subjects ..... 36
4.2. Application of Poincaré Plot to Assess PPGA in Smokers and Non-smokers (Study Part Two) ....... 37
4.3. Study Limitations .... 38
4.3.1. Study Limitations in Study Part One .... 38
4.3.2. Study Limitations in Study Part Two ... 38
Chapter 5. Conclusion .. 39
References ..... 40

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