臺灣博碩士論文加值系統

根據世界衛生組織統計，全球每年約有1650萬人死於心血管疾病，是蟬聯十大死因中的前三名。相較於其他病症，心血管疾病較無明顯的病徵，難以受一般民眾察覺，是不可輕忽的重大疾病。
如何診斷、預防心血管疾病，血壓被視為是一項重要的反應指標。然而，血壓量測需要相關的醫療設備和具備醫療知識的護理人員才可進行，且僅有侵入式量測才可取得連續時間的血壓資訊。
本論文探討的課題便是如何進行非侵入式的連續血壓預測。使用者不需至醫院，也可進行血壓量測。為此必須結合人體心電訊號、光體積變化描記圖，取得血壓推估參數。透過MIMIC資料庫所提供的病患數據進行血壓預測模型的分析與修正，進而達成非侵入式的連續血壓推估之方法。

According to the statistics of World Health Organization, there are about 1650 people die from cardiovascular disease each year and it is reselected as top three of the 10 causes of death. Compared to other diseases, cardiovascular disease is difficult to detect by the general public since it is less obvious symptoms. In other words, Cardiovascular disease is a major disease can not be ignored.
How to diagnose and prevent cardiovascular disease, blood pressure is considered as an important indicator. However, conduct of blood pressure measurements needs medical equipment and nurses with medical knowledge, and only invasive blood pressure measurement made available continuous-time information.
In this thesis, the research issue is to propose a non-invasive method to get continuous blood pressure measurements. Although users do not go to the hospital, they still can measure their blood pressure. To achieve this goal, we combine human ECG signal and the PPG signal to get the parameters of estimation blood pressure model. We use patient data provided from MIMIC database to analysis and correct our proposed blood pressure estimation models to provide a non-invasive method of continuous blood pressure measurements.

致謝辭 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 xii
一、 緒論 1
1.1. 研究背景 1
1.2. 研究動機與目的 2
1.3. 論文架構 3
二、 相關研究 5
2.1. 心電圖 5
2.1.1. 心電圖介紹 5
2.1.2. 十二導程 6
2.1.3. 心電圖特徵 8
2.1.4. 心電圖特徵擷取方法 9
2.2. 光體積變化描記圖 11
2.2.1. PPG介紹 11
2.2.2. PPG特徵及擷取方法 12
2.3. 臨床醫學血壓量測現況 14
2.3.1. 非侵入式血壓量測原理 15
2.3.2. 侵入式血壓量測原理 15
2.3.3. 血壓推估方法 16
三、 血壓推估方法 21
3.1. 心電圖特徵偵測 21
3.1.1. 心電圖(ECG) 21
3.1.2. 心電圖擷取目標 21
3.1.3. Tompkins演算法 22
3.1.4. 針對血壓量測模型之R波偵測 27
3.2.光體積變化描記圖特徵偵測 32
3.2.1. 光體積變化描記圖(PPG) 32
3.2.2. PPG擷取目標 33
3.2.3. 帶通濾波器 34
3.2.4. PPG特徵偵測 35
3.3. 血壓量測模型 38
3.3.1. 血壓量測模型輸入變數 38
3.3.2. 血壓量測模型 43
3.3.3. RLS演算法校正 45
3.3.4. 模型訓練預處理 46
3.3.5. 血壓模型訓練與比較 47
3.3.6. 連續血壓波形模型 50
四、 實驗結果 52
4.1. ECG和PPG特徵偵測驗證 52
4.1.1. 傅立葉ECG模擬 52
4.1.2. 針對多種心血管疾病之ECG模擬及R波偵測方法驗證 54
4.1.3. 驗證用PPG訊號 62
4.1.4. PPG特徵點驗證 62
4.2. 血壓推估實驗 64
4.2.1. 實驗樣本 64
4.2.2. ECG特徵偵測結果 65
4.2.3. PPG特徵偵測結果 66
4.2.4. 血壓量測結果 68
4.2.5. 方法比較 73
4.2.6. 連續血壓量測結果 75
4.2.7. 實體裝置血壓推估實驗 78
五、 結論與未來展望 82
5.1. 結論 82
5.2. 未來展望 83
參考文獻 85

[1]World Health Orgnaization. Media centre. The top 10 causes of death. Available at: http://www.who.int/mediacentre/factsheets/fs310/en/. Accessed May 2014.
[2]Robert E. Kleiger, J. Philip Miller, et al., “Decreased Heart Rate Variability and Its Association with Increased Mortality after Acute Myocardial Infarction,” The American Journal of Cardiology, vol. 59, pp. 256-262, 1987.
[3]V. Vijaya, K. Kishan Rao and P. Sahrudai, “Identification of Sudden Cardiac Arrest Using the Pan-Tompkins algorithm,” IEEE, International Conference on Computer Modelling and Simulation, pp. 97-100, 2012.
[4]Dong-Yu Zhang and Wang-Meng Zuo, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” J. Biomedical Science and Engineering, pp. 361-366, 2010.
[5]Shivaraman Ilango and Pooja Sridhar, “A Non-Invasive Blood Pressure Measurement using Android Smart Phones,” IOSR Journal of Dental and Medical Sciences, vol. 13, pp. 28-31, 2014.
[6]Philip de Chazal, Conor Heneghan, et al., “Automated Processing of the Single-Lead Electrocardiogram for the Detection of Obstructive Sleep Apnoea,” IEEE, Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 686-696, 2003.
[7]Marek Malik, PhD, el at., “Heart rate variability,” European Heart Journal, pp.354-381, 1996.
[8]Gary G. Berntson and J. Thomas Bigger, “Heart rate variability origins, methods, and interpretive caveats,” Psychophysiology, vol. 34, no. 6, pp. 623-648, 1997.
[9]Ching-Kun Chen, Chun-Liang Lin, et al., “A Chaotic Theoretical Approach to ECG-Based Identity Recognition,” IEEE, Computational intelligence magazine, vol. 9, pp. 56-63, 2014.
[10]許鴻義, “心電圖訊號量測系統之製作與分析,” 碩士論文, 機械工程系碩士班, 國立雲林科技大學, 雲林縣, 2003.
[11]Yan Sun, Kap Luk Chan and Shankar Muthu Krishnan, “Characteristic wave detection in ECG signal using morphological transform,” BMC Cardiovascular Disorders, pp. 1-7, 2005.
[12]M. K. Islam, A. N. M. M. Haque, el at., “Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools,” International Journal of Computer and Electrical Engineering, vol. 4, no. 3, pp. 404-408, 2012.
[13]B. Priyadarshini, R.K.Ranjan and Rajeev Arya, “Determining ECG characteristics using wavelet transforms,” International Journal of Engineering Research & Technology, vol. 1, no. 6, pp. 1-8, 2012.
[14]曾文慶, 謝瑞建, “以小波轉換為基礎之12島成心電圖特徵點萃取”, 碩士論文,資訊工程學系, 中華大學, 新竹市, 2006.
[15]Willis J. Tompkins, “Biomedical Digital Signal Processing,” Prentice Hall, pp. 236-263, 2000.
[16]M.A.Hasan, M.B.I. Reaz and M. I. Ibrahimy, “Fetal Electrocardiogram Extraction and R-Peak Detection for Fetal Heart Rate Monitoring using Artificial Neural Network and Correlation” , International Joint Conference on Neural Networks, pp. 15-20, 2011.
[17]Kristjan Pilt, Rain Ferenets, el at., “New Photoplethysmographic Signal Analysis algorithm for Arterial Stiffness Estimation,” Hindawi Publishing Corporation the ScientificWorld Journal, pp. 1-9, 2013.
[18]Q. Yousef, M.B.I.Reaz and M.A.M.Ali, “The Analysis of PPG Morphology: Investigating the Effects of Aging on Arterial Compliance,” Measurement Science Review, vol. 12, no. 6, pp. 266-271, 2012.
[19]Y. K. Qawqzeh, M. B. I. Reaz and M. A. M. Ali, “The analysis of PPG contour in the assessment of atherosclerosis for erectile dysfunction subjects,” WSEAS Transactions on Biology and Biomedicine, vol. 7, no. 4, pp. 306-315, 2010.
[20]S.P. Reddy, L.Y. Shyu, B.E. Hurwitz, J.H. Nagel and N. Schneidennan, “Improved Reliability of Impedance Cardiography by New signal Processing Techniques,” IEEE , Proceedings of the Annual International Conference, vol. 1,pp. 43-44, 1988.
[21]林育德, “多訊息脈搏血氧濃度計之設計,” 碩士論文,自動控制工程學系碩士班, 逢甲大學, 台中市, 2010.
[22]呂宗憲, 林筱莉, 陳智傑, "ECG與PPG信號之相關性研究", 專題論文,自動控制工程學系, 逢甲大學, 台中市, 2003.
[23]Hui-yan WANG and Pei-yong ZHANG, “A model for automatic identification of human pulse signals,” Journal of Zhejiang University Science A, vol 9, no.10 , pp. 1382-1389, 2008.
[24]Mandeep Singh and ShivangiNagpal, “Features Extraction in Second Derivative of Finger PPG Signal: A Review,” International Journal of Computer Science & Communication, vol. 4, no. 2, pp. 1-5, 2013.
[25]Mohamed Elgendi, “On the Analysis of Fingertip Photoplethysmogram Signals,” Current Cardiology Reviews, pp. 14-25, 2012.
[26]Heiko Gesche, Detlef Grosskurth, et al., “Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method,” European Journal of Applied Physiology, vol. 112, no. 1, pp. 309-315, 2011.
[27]Yan Chen, Changyun Wen, Haitao Tang and Linda Xiuzhen Teng, “The Relationship between Different Pulse Wave Velocity and Systolic/Diastolic Pressure,” IEEE, Conference on Industrial Electronics and Applications, pp. 1185-1190, 2008.
[28]Xiaochuan He, Rafik A. Goubran, et al., “Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement,” IEEE, Instrumentation and Measurement, vol. 63, no. 6, pp. 1431-1439, 2014.
[29]Sujay Deb, Chinmayee Nanda, et al., “Cuff-less Estimation of Blood Pressure using Pulse Transit Time and Pre-Ejection Period,” IEEE, International Conference on Convergence Information Technology, pp. 941-944, 2007.
[30]J. Muehlsteff, X.L. Aubert and M. Schuett, “Cuffless Estimation of Systolic Blood Pressure for Short Effort Bicycle Tests: The Prominent Role of the Pre-Ejection Period,” IEEE, Annual International Conference of the Engineering in Medicine and Biology Society, pp. 5088-5092, 2006.
[31]Adriana Arza and Jesús Lázaro, “Pulse Transit Time and Pulse Width as Potential Measure for Estimating Beat-to-Beat Systolic and Diastolic Blood Pressure,” IEEE, Computing in Cardiology Conference, pp. 887-890, 2013.
[32]Braiam Escobar and Robinson Torres, “Feasibility of non-invasive blood pressure estimation based on pulse arrival time: a MIMIC database study,” Computing in Cardiology Conference, pp. 1113-1116, 2014.
[33]Federico S. Cattivelli and Harinath Garudadri, “Noninvasive Cuffless Estimation of Blood Pressure from Pulse Arrival Time and Heart Rate with Adaptive Calibration,” IEEE, Wearable and Implantable Body Sensor Networks, pp. 114-119, 2009.
[34]Jiapu Pan and Willis J.Tompkins, “A Real-Time QRS Detection algorithm,” IEEE, Transactions on Biomedical Engineering, vol. BME-32, no. 3, pp. 230-236,1985.
[35]Nehla Debbabi, Sadok El Asmi and Hichem Arfa, “Correction of ECG baseline wander Application to the Pan & Tompkins QRS detection algorithm,” International Symposium on I/V Communications and Mobile Network, pp. 1-4, 2010.
[36]莊芳甄, 張瓊仁, “平均心電軸之自動檢測系統研製,” 專題論文,自動控制工程學系, 逢甲大學, 台中市, 2003.
[37]Thakor, N. V., Webster, J. G., and Tompkins, W. J. “Optimal QRS detector.” Medical and Biological Engineering , pp. 343-50, 1983.
[38]Chun-Lung Chang, Kang-Ping Lin, el at., “Validation of Automated Arrhythmia Detection for Holter ECG,” IEEE, Engineering in Medicine and Biology Society, vol. 20, no. 1, pp. 101-103, 1998.
[39]Kang-Ping Lin, Chang and Walter H., “A technique for automated arrythmia detection of Holter ECG,” IEEE, Annual Conference Engineering in Medicine and Biology Society, vol. 1, pp. 183-184, 1995.
[40]Devy Widjaja, Steven Vandeput, et al., “Accurate R Peak Detection and Advanced Preprocessing of Normal ECG for Heart Rate Variability Analysis,” IEEE, Conference on Computing in Cardiology, pp. 533-536, 2010.
[41]R. Laulkar and N. Daimiwal, “Application of finger photoplethysmography,” International Journal of Engineering Research and Applications, vol. 2, no. 1, pp. 877-880, 2012.
[42]J. Zheng, S. Hu, S. Xin, V. P. Crabtree and P. R. Smith, “Non-invasive photoplethysmography to assess lower limb peripheral perfusion with selected postural changes,” Physiology Measurement.
[43]Dae-Geun Jang, Sangjun Park, and Minsoo Hahn, “A Real-Time Pulse Peak Detection algorithm for the Photoplethysmogram”, International Journal of Electronics and Electrical Engineering, vol. 2, no. 1, pp. 45-49, 2014.
[44]Enze Yu, Dianning He, et al., “Feasibility Analysis for Pulse Rate Variability to Replace Heart Rate Variability of the Healthy Subjects,” IEEE, International Conference on Robotics and Biomimetics, pp. 1065-1070, 2013.
[45]M. Y. M. Wong and Y. T. Zhang, “The Correlation Study on the Relationship between Pre-Ejection Period and Arterial Blood Pressure,” IEEE, Medical Devices and Biosensors, pp. 92-93, 2008.
[46]W. Chen, T. Kobayashi, et al., “Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration,” Medical and Biological Engineering and Computing, vol. 38, no. 5, pp. 569-574, 2000.
[47]R. A. Payne, C. N. Symeonides, D. J. Webb and S. R. J. Maxwell, “Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure,” Journal of Applied Physiology, pp. 616-625, 2005.
[48]Univ.Prof., Di Dr.-lng. and Markus Rupp, “Adaptive Filters,” Institute og Telecommunications, 2011.
[49]Yunmin ZHU and X. Rong Li, “Recursive Least Squares with Linear Constraints,” Communications in Information and Systems, vol.7, no. 2, pp. 287-312, 2007.
[50]Bryan Williams, Peter S. Lacy, et al., “Development and Validation of a Novel Method to Derive Central Aortic Systolic Pressure From the Radial Pressure Waveform Using an N-Point Moving Average Method,” Journal of the American College of Cardiology, vol. 57, no. 8, pp. 951-961, 2011.
[51]Choon Meng Ting, Ngak Hwee Chua and Wee Leng Peh, “Method of deriving central aortic systolic pressure values and method for analysing an arterial dataset to derive the same,” U.S. patents 20110230773 A1.
[52]Cheol Jeong, Jae Il Ko, Sung Oh Hwang and Hyung Ro Yoon, “A New Method to estimate Arterial Blood Pressure using Photoplethysmographic Signal,” IEEE, Conference Publications, pp. 4667-4670, 2006.
[53]Shine Augustine and Manimegalai .P, “Non Invasive Estimation of blood pressure using a linear regression model from the photoplethysmogram (PPG) Signal,” International Conference on Information Technology, pp. 31-35, 2013.
[54]Patrick E. McSharry, Gari D. Clifford, et al., ”A Dynamical Model for Generating Synthetic Electrocardiogram Signals,” IEEE, Transactions on Biomedical Engineering, vol. 50, no. 3, 2003.
[55]Gade S. S., Shendage S. B. and Uplane M. D., “Mathematical Model of ECG Signal for OFF Line Adaptive Signal Processing in MATLAB,” International Journal of Modern Engineering Research, vol. 2, no. 4, pp. 1944-1946, 2012.
[56]R. Karthik, “ECG Simulation Using MATLAB,” College of Engineering, Guindy, Anna University, 2006.
[57]Gari D. Cli ord and Daniel J. Scott, Mauricio Villarroel, "User Guide and Documentation for the MIMIC II Database", Harvard-MIT Division of Health Sciences & Technology and Massachusetts Institute of Technology Technology, 2012.