臺灣博碩士論文加值系統

社會人口老齡化所帶來的沉重醫療負擔將會成為下一代夢魘，也為現存醫療體系帶來巨大挑戰，社會急需低成本的疾病預防與居家照護方案，降低未來可能的醫療負擔與門診壓力。而近年來，國內心血管疾病長居國人十大死因，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，同時各國亦遭遇其衝擊，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。世界各國的醫療體系亟需獲取早期預防的工具降低心血管疾病風險，以及更廉價可靠的醫療設備。
本論文提出一即時基於總體經驗模態分解的有效PPG訊號處理系統實現多生理參數量測，藉由近紅外線得知微血管因受心臟收縮、舒張而改變之血流容積，其變化可以顯示體表動脈脈波的資訊，並運用希爾伯-黃轉換演算法並使用台灣獨步全球的半導體產業技術開發數位訊號處理系統單晶片設計以取得高品質訊號，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PPG訊號處理系統包含一PPG訊號擷取電路，一總體經驗模態分解晶片，一無線藍芽傳輸模組，以及一GUI顯示介面。PPG訊號由手指經由PPG訊號擷取電路量測並轉換為數位訊號後，傳送至總體經驗模態分解晶片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。
本發明為評估潛在心血管疾病的風險，透過總體經驗模態分解拆解出具有生理意義的本質函數，並進一步分析時域與頻域的參數如HR、RI、SI、LF、HF、VHF作為心血管疾病診斷的依據，且於系統加設無線傳輸以實現長時間居家生理照護。

The heavily medical burden caused by population ageing will become a serious challenge for the current and next generation medical care system. There is an urgent need of low-cost disease prevention and home care programs to lower the possible medical burden in the future. The cardiovascular diseases have been on the list of leading cause of death for years in Taiwan. There is about seventeen million people pass away because of cardiovascular around the world. There is urgent need to get the early prevention tool to reduce the risk of cardiovascular disease all over the world.
An effective photoplethysmography (PPG) signal processing system based on ensemble empirical mode decomposition (EEMD) method for acquiring the multiple physiological parameters is proposed in this project. The information of arterial pulse can be obtained by near-infrared. A high quality signal can be extracted through the proposed EEMD algorithm. Based on the most advanced semiconductor industry in Taiwan, the regulation of autonomic nervous system (ANS), RI and SI can be derived in real-time and monitored continuously. It makes the at-home care possible and lowers the rate of cardiovascular diseases and medical expenses through long-term monitoring.
PPG signal acquired by the PPG capture circuit is sampled through the ADC at sample frequency of 200Hz after being filtered by the band pass filter. The digitized data are decomposed into IMFs with physiological meanings by the EEMD IC. The output IMFs are wirelessly sent to a computer via a Bluetooth module. Then the regulation of autonomic nervous system , RI and SI can be derived and display on the GUI.
To overcome the noise and aliasing effect caused by nonstationary signals, many innovative and effective modules were developed in this thesis. The proposed HHT SoC design could be implemented in hardware with limited resources and fabricated under TSMC 90 nm CMOS technology.
To assess the potential risk of cardiovascular, the IMFs with physiological meanings can be extracted from PPG. The RI, SI, LF, HF and VHF can be derived as the parameters to help the diagnosis of cardiovascular disease.

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 Photoplethysmography 6
1.3 Scope and Contributions 10
1.4 Organization of this Thesis 11
Chapter 2 Methods 12
2.1 Ensemble Empirical Mode Decomposition 12
Chapter 3 System Architecture and Implementation 15
3.1 System Overview 15
3.2 Front-end Circuit 16
3.2.1 PPG Capture Circuit 16
3.2.2 A Band-pass Filter 17
3.2.3 Operational Amplifier 18
3.2.4 Analog to Digital Converter 18
3.3 EEMD IC 19
3.4 Wireless Transmission Module 21
3.5 Graphical User Interface 22
Chapter 4 System Verification and Result 25
4.1 Simulation and Hardware Experiment 25
4.1.1 Simulation Results 25
4.1.2 Hardware Experiment Result 26
Chapter 5 IRB 29
5.1 Subjects and Data Collection 29
5.2 Consistency Analyze 29
5.3 iPRV 34
5.4 Support Vector Machine 41
Chapter 6 Conclusion and Future Work 46
6.1 Conclusion 46
6.2 Future Work 46
References 47

[1] 衛生福利部統計部, “死因統計上卷,”2013. [Online]. Available:
http://www.mohw.gov.tw/cht/DOS/Statistic.aspx?f_list_no=312&;fod_list_no=1610
[2] 內政部戶政司, “人口年齡結構指標,”2013. [Online]. Available:
http://www.ris.gov.tw/zh_TW/346
[3] O’Rourke, M.F., et al., “Clinical applications of arterial stiffness; definitions and reference values, ” American Journal of Hypertension, pp 426-444, 2002.
[4] CHANG, Chia-Chi; HSIAO, “Tzu-Chien; HSU, Hung-Yi. Frequency range extension of spectral analysis of pulse rate variability based on Hilbert–Huang transform,” Medical &; biological engineering &; computing, 2014, 52.4: 343-351.
[5] GIL, E., et al. “Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions,” Physiological Measurement, 2010, 31.9: 1271.
[6] J. Fortin, W. Marte, R. Grüllenberger, A. Hacker, W. Habenbacher, A. Heller, CH. Wagner, P. Wach, F. Skrabal, “Continuous non-invasive blood pressure monitoring using concentrically interlocking control loops,” Computers in Biology and Medicine, 2006.
[7] E. Sackl-Pietsch, “Continuous non-invasive arterial pressure shows high accuracy in
comparison to invasive intra-arterial blood pressure measurement,” Department of
Anesthesiology, Landeskrankenhaus Bruck an der Mur.
[8] Inc. Biosign Technologies, “Pulsewave™MAX,”Biosign Technologies Inc., 2013. [Online]. Available: http://www.biosign.com/pulsewave-max.aspx
[9] K. Xu, Q. Zhou, H. F. Yang, “A Novel Method Reflecting Arterial Blood Pressure Changes Based on FFT Analysis for Single Pulse Waveform,” The 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 1205,2308 16-18 May 2008.
[10] HUANG, Norden E., et al. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society, 1998. p. 903-995.
[11] WU, Zhaohua; HUANG, Norden E. “Ensemble empirical mode decomposition: a noise-assisted data analysis method.” Advances in adaptive data analysis, 2009, 1.01: 1-41.
[12] Cormack G. C., The Arterial Anatomy of Skin Flaps, Churchill Livingstone, 1994.
[13] B.Hertzman A., “The Blood Supply of Various Skin Areas as Estimated by the Photoelectric Plethysmograph,” American Journal of Physiology, pp. 328–340, 1938.
[14] 林育德, "脈波信號與PPG信號特徵之相關性研究,"中醫藥年報, 第 冊第一冊, pp. 361-388,2010.
[15] 蔡青哲, PPG 的移動干擾偵測與信號重建, 逢甲大學自動控制工程學系碩士論文, 2009
[16] N. E. Huang and N. O. Attoh-Okine (ed.), Hilbert–Huang Transform in Engineering. CRC Press, 2005, p. 313.
[17] N. E. Huang , Z. Shen , S. R. Long , M. C. Wu , H. H. Shih , Q. Zheng , N. C. Yen , C. C. Tung and H. H. Liu “The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis”, Proc. R. Soc. Lond. A, Math.Phys. Sci., vol. 454, no. 1971, pp.903 -995 1998.
[18] P. Flandrin, G. Rilling and P. Gon¸calv`es, Empirical mode decomposition as a filterbank, IEEE Signal Process.Lett. 11 (2004) 112–114.
[19] P. Flandrin, P. Gon¸calv`es and G. Rilling, EMD equivalent filter banks, from interpretationto applications, in Hilbert–Huang Transform: Introduction and Applications, eds. N. E. Huang and S. S. P. Shen (World Scientific, Singapore, 2005), pp. 67–87.
[20] Wu, Zhaohua, and Norden E. Huang. “A study of the characteristics of white noise using the empirical mode decomposition method.” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 460.2046 (2004): 1597-1611.
[21] User manul of ADS1298: Low-Power, 8-Channel, 24-Bit Analog Front-End for Biopotential Measurements.
[22] UART Protocol, [Online]. Available: http://en.wikipedia.org/wiki/Universal_asynchronous_receiver/transmitter
[23] Højgaard MV, Holstein-Rathlou NH, Agner E, Kanters JK “Reproducibility of heart rate variability, blood pressure variability and baroreceptor sensitivity during rest and head-up tilt. ” Blood Press Monit 10:19–24
[24] S.C. MILLASSEAU, R.P. KELLY, J.M. RITTER, P.J. CHOWIENCZYK, “Determination of age-related increases in large artery stiffness by digital pulse contour analysis,” Clinical Science Oct 2002, 103 (4) 371-377
[25] nGenuity™ 8100E Series, [Online]. Available:
http://www.criticaresystems.com/products/patient-monitors/ngenuity-8100e-series/
[26] HUANG, Norden E., et al. “On instantaneous frequency”. Advances in adaptive data analysis, 2009, 1.02: 177-229.9
[27] Task Force of the European Society of Cardiology and NASPE, “Heart rate variability, standards of measurement, physiological interpretation and clinical use,” Circulation, 93(5):1043-1065,1996.
[28] GG Berntson, Bigger JT Jr, DL Eckberg, P Grossman, PG Kaufmann, M Malik, HN Nagaraja, SW Porges, JP Saul, PH Stone, and MW van der Molen, “Heart rate variability: origins, methods, and interpretive caveats,” Phychophysiology, 34(6):623-648,1997.
[29] H Li, S Kwong, L Yang, D Huang, and D Xiao, “Hilbert-Huang transform for analysis of heart rate variability in cardiac health, ” IEEE/ACM Transsactions on Computational Biology and bioinformatics, 8(6):1557-1567, 2011.
[30] Gil E, Orini M, Bailon R, Vergara JM, Mainardi L, Laguna P (2010) “Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. ” Physiol Meas 31:1271–1290
[31] Sangodiah, A.; Ahmad, R.; Ahmad, W.F.W., “A review in feature extraction approach in question classification using Support Vector Machine, ” in Control System, Computing and Engineering (ICCSCE), 2014 IEEE International Conference on , vol., no., pp.536-541, 28-30 Nov. 2014
[32] S.C. MILLASSEAU, R.P. KELLY, J.M. RITTER, P.J. CHOWIENCZYK, “Determination of age-related increases in large artery stiffness by digital pulse contour analysis,” Clinical Science Oct 2002, 103 (4) 371-377