臺灣博碩士論文加值系統

隨著科技進步與發展，現今社會所面臨的人口老齡化議題以及其所帶來的沉重醫療負擔將會為現存醫療體系以及我們的下一代，帶來巨大挑戰與夢靨。在106年國人十大死因中，因心血管相關疾病(Cardiovascular disease, CVDs)占總死亡人數的48.5 %，心血管疾病發生的原因為血管彈性逐漸減弱，造成血液流速上升、血壓身高，引發高血壓、中風等疾病。且心血管疾病除了受死亡的威脅之外，還會因心血管疾病而引發其它病症進而對病患及家人造成沉重的負擔。在國際上，每年全世界約有一千七百萬人死於心血管疾病，並帶來沉重的醫療負擔甚至損害勞動力。現今社會在疾病預防與居家照護方面之研發，著重於運用創新的技術開發出一高整合度的健康照護系統已成為研究人員非常重要課題。促使降低未來可能的醫療負擔與門診壓力。
本論文將提出一應用於心電圖訊號與光電容積描記法訊號之基於總體經驗模態分解訊號處理整合系統實現及驗證。生理訊號為非穩態和非線性時間訊號，在分析應用上，總體經驗模態分解法已被證明為可以有效且可調變式分析此類訊號，應用此演算法分析心電圖訊號與光電容積描記法訊號可有效的將原始訊號分解成此演算法之最基本的分析要素-時間本質函數(Intrinsic Mode Functions, IMF)。針對分解後的不同時間區段之時間本質函數進行分析並找出其訊號中所對應的生理資訊。由於總體經驗模態分解法在運算上面具有高複雜度及迴圈式的運算流程，為達到高整合度的即時的訊號處理與分析設計，本論文應用FPGA設計之超大型積體電路的技術來實現此系統以取得高品質訊號，進而分析出自律神經的調節平衡、動脈彈性指標、動脈硬化指標等心血管疾病指標，使醫護人員可以透過此系統對使用者提出建議與診斷。
本論文所提出之ECG與PPG訊號處理整合系統包含PPG訊號擷取電路、ECG訊號擷取電路、總體經驗模態分解處理器、無線藍芽傳輸模組以及GUI顯示介面。前端訊號擷取ECG訊號與PPG訊號轉換成數位訊號並且同步接收後，傳送至總體經驗模態分解處理器分解出具有生理意義之本質函數後，再經由無線藍芽模組傳送至電腦，透過GUI介面顯示心跳、頻譜分析、動脈彈性指標與動脈硬化指標。
本設計為心血管疾病相關檢測與評估風險與檢測，透過總體經驗模態濾除雜訊與基線飄移並且分解出具有生理意義的本質函數，並且以心率(HR)、血管彈性指數(RI)、血管硬化指數(SI)作為心血管疾病診斷的依據，並且以藍芽模組以實現無線傳輸功能，達到長時間居家生理照護的需求。

With the advancement and development of science and technology, the issue of population aging faced by today's society and the heavy medical burden it brings will bring enormous challenges and nightmares to the existing medical system and our next generation. According to statistics regarding the ten leading causes of death among the Taiwanese people, announced by the Department of Health, Executive Yuan, mortalities due to cardiovascular disease (CVDs) accounted for 48.5% of the total number of deaths in 2017. In addition to the threat of death, cardiovascular disease also causes other diseases caused by cardiovascular disease, which in turn imposes a heavy burden on patients and their families. Internationally, about 17 million people worldwide die from cardiovascular disease every year, and they bring a heavy medical burden and even damage the labor force. These health concerns may also induce disease and afflictions such as hypertension, stroke, and diabetes. Therefore, innovative health-care systems with high integrative have become an important topic of research in recent years.
This thesis presents an implementation and verification based on the Ensemble Empirical Mode Decomposition (EEMD) signal processing integration system for Electrocardiography(ECG) signals and Photoplethysmography(PPG) signals. The physiological signals are non-linear and non-stationary time signals. In analytical applications, the ensemble empirical mode decomposition method has been proven to be an effective and variably variable analysis of such signals. The analysis of the ECG signals and the PPG signals by EEMD algorithm can effectively decompose the raw signals into the most essential analysis component of the algorithm, the Intrinsic Mode Functions (IMF). The algorithm can analyze the time-equivalent function of different time segments after decomposition and also find the physiological information corresponding to the signal.
Because the ensemble empirical mode decomposition method has high computation complexity and recursively operation, in order to achieve high integration of real-time signal processing and analysis design, this thesis applies the technology of very-large-scale integrated circuit designed by FPGA to realize this system to obtain the high-quality signal. Furthermore, cardiovascular disease indicators such as the regulation balance of autonomic nerves, arterial reflection index, and arterial stiffness index are analyzed, so that medical staff can make recommendations and diagnoses to users through this system.
The ECG and PPG signals processing integration system proposed in this thesis includes a PPG signals acquisition circuit, an ECG signals acquisition circuit, an EEMD processor, a wireless Bluetooth transmission module, and a Graphical User Interface(GUI). The ECG signals and the PPG signals synchronously extracted from front-end are converted into a digital signal and, and transmitted to the EEMD processor to decompose the physiologically significant IMFs, then transmitted to the computer via the wireless Bluetooth module. It shows heart rate, spectrum analysis, Arterial elasticity index, and arteriosclerosis index through the GUI.
This design is for the detection and assessment of cardiovascular disease-related risks and detection. It filters out the noise and baseline drift through the EEMD and decomposes the physiologically significant IMFs, and takes heart rate (HR), vascular reflection index (RI), vascular stiffness index (SI) as the basis for the diagnosis of cardiovascular disease, and the use of Bluetooth module to achieve wireless transmission function, to to meet the needs of long-term home care.

Table of Contents
中文摘要 i
Abstract iii
誌謝 v
Table of Contents vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1 Preface 1
1.2 Electrocardiography 5
1.3 Photoplethysmography 6
1.4 Electrocardiography and Photoplethysmography Signals Processing 8
1.5 Scope and Contributions 9
1.6 Organization of this Thesis 10
Chapter 2 Algorithms and Methods 11
2.1 Empirical Mode Decomposition 11
2.2 Ensemble Empirical Mode Decomposition 13

Chapter 3 System Architecture 16
3.1 System Overview 16
3.2 Front-end Circuit 17
3.2.1 ECG signals acquisition circuit 17
3.2.2 PPG signals acquisition circuit 19
3.2.3 ECG / PPG Front-end circuit 22
3.3 Ensemble Empirical Mode Decomposition Processor 23
3.3.1 Data Normalization Unit 24
3.3.2 White Noise Unit 25
3.3.3 Sifting Process Unit 26
3.3.4 Memory Management Unit 28
3.3.5 Reconstruction Output Unit 29
3.3.6 System Control Unit 30
3.3.6 Real-time EEMD 30
3.4 Wireless Transmission Module 34
3.5 Graphical User Interface 35
Chapter 4 Physiological Indexes Extraction 38
4.1 Peak Detection Method 38
4.2 Peak to Peak Interval 39
4.3 Pulse Transmit Time 40
4.4 Heart Rate 41
4.5 Reflection index and Stiffness index 42
4.6 Blood Pressure 43
Chapter 5 Result and Discussion 45
5.1 Simulation and Hardware Experiment 45
5.2 Simulation Results 48
5.3 Comparison Results 57
5.4 Experimental Results 58
Chapter 6 Conclusion and Future Work 65
6.1 Conclusion 65
6.2 Future Work 65
References 67



 

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