基於深度學習的即時血壓估測演算法

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姓名

卡提卡(Kartika Purwandari) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於深度學習的即時血壓估測演算法
(Real-Time Blood Pressure Estimation Using Deep Learning)

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摘要(中)

血壓監測是監測人們健康狀況的方法之一。異常血壓是各種心血管疾病的症狀。早期發現高血壓可以幫助患者早日治療並降低死亡率。因此，具有對患者的血壓變化進行實時監測的機制是非常有價值的。在本論文中，我們提出了一個基於心電圖（ECG）和脈搏傳導時間（PTT）的深度學習模型，用於連續估計收縮壓（SBP）和舒張壓（DBP）值。我們還進行了實驗來比較傳統機器學習方法和深度學習模型之間的準確性。在過去，許多研究人員已經嘗試在深度學習上進行殘餘連接以提高模型的性能。我們遵循這樣的做法並在每層LSTM中添加殘留物。對於實驗，我們使用Physionet的多參數智能監護在重症監護（MIMIC）II中的數據集作為心電圖（ECG）和光電容積描記圖（PPG）信號以及動脈血壓（ABP）信號的來源。我們使用了包括三個信號的12.000個主題。實驗結果表明，該方法可以將SBP的平均絕對誤差（MAE）值降至0.7357 mmHg，DBP降低0.5587 mmHg。我們還計算標準偏差（STD）值。 SBP的STD值為0.9579 mmHg，DBP的STD值為0.5088 mmHg。

摘要(英)

Blood pressure monitoring is one of the ways to monitor people’s health conditions. The abnormal blood pressure is a symptom of various cardiovascular diseases. Early detection of hypertension can help patients get early treatment and reduce mortality. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for the change of blood pressure in patients. In this thesis, we present a deep learning models based on electrocardiogram (ECG) and pulse transit time (PTT), for the continuous estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We also performed experiments to compare the accuracy between the traditional machine learning method and deep learning model. In the past, many researchers already try residual connection on deep learning to increase the performance of the model. We followed such practice and added the residual inside each layer of LSTM. For the experiments, we used the dataset of Physionet’s multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II as the source of electrocardiogram (ECG) and photoplethysmogram (PPG) signals as well as the arterial blood pressure (ABP) signal. We used 12.000 subjects that include three signals. The experimental results show that the proposed method can reduce the value of mean absolute error (MAE) to 0.7357 mmHg for SBP and 0.5587 mmHg for DBP. We also calculate the standard deviation (STD) value. The STD values are 0.9579 mmHg for SBP and 0.5088 mmHg for DBP.

關鍵字(中)

★ deep learning
★ blood pressure estimation
★ LSTM
★ bidirectional LSTM

關鍵字(英)

★ deep learning
★ bidirectional LSTM
★ LSTM
★ blood pressure estimation

論文目次

摘要 i
ABSTRACT ii
ACKNOWLEDGMENT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
CHAPTER I 1
INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Anatomy and Physiology of the Heart 2
1.3 Biomedical Signal 4
1.3.1 Electrocardiogram (ECG) Signal 4
1.3.2 Photoplethysmogram (PPG) Signal 7
1.3.3 Arterial Blood Pressure (ABP) Signal 10
1.4 Heart Rate 11
1.5 Pulse Transit Time 12
CHAPTER 2 13
LITERATURE REVIEW 13
2.1. Research Purposes 13
2.2. Signal Preprocessing 13
2.3. Feature Analysis 15
2.4. Machine Learning 17
2.4.1. Regression 17
2.4.2. Deep Learning 18
CHAPTER 3 21
METHODOLOGY 21
3.1. Fast Fourier Transform Concept 21
3.2. Multiple Linear Regression (MLR) 21
3.3. Regression Ensemble Learning 22
3.4. Deep LSTM Layer 23
3.5. Bidirectional LSTM 25
3.6. Residual Connected 26
CHAPTER 4 27
RESULTS AND DISCUSSIONS 27
4.1. Experimental Setup 27
4.1.1. Environment 28
4.1.2. Dataset 28
4.1.3. Preprocessing 29
4.1.4. Features Extraction 30
4.2. Experimental Results 31
4.3. Discussions 34
4.3.1. Deep Learning vs. Traditional Regression Methods 34
CHAPTER 5 37
CONCLUSIONS 37
REFERENCES 38

參考文獻

[1] Cardiovascular diseases (CVDs),
https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
[2] H. Houssein Essam, M. Kilany, and H. Aboul Ella, “ECG Signals Classification,” Int. J. Intelligent Engineering Informatics, vol. 5(4), pp. 376-396, Jan. 2017.
[3] J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiological Measurement, vol. 28, pp. R1–R39, Feb. 2007. Available: stacks.iop.org/PM/28/R1
[4] B. Zhang, Z. Wei, J. Ren, et al., “An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees,” IEEE Special Section on Human-Centered Smart System and Technologies, vol. 6, pp. 21758-21768, Jan. 2019. Available: https://core.ac.uk/download/pdf/153388866.pdf
[5] Highlight from the 2017 Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults, https://professional.heart.org/professional/ScienceNews/UCM_496965_2017-Hypertension-Clinical-Guidelines.jsp? UTM_source=Postcard&utm_medium=Print&utm_campaign=Hypertension
[6] M. Kachuee, M. Mahdi Kiani, H. Mohammadzade, M. Shabany. (2015, Jul.). “Cuff-Less High-Accuracy Calibration-Free Blood Pressure Estimation Using Pulse Transit Time,” IEEE International Symposium on Circuits and Systems (ISCAS), vol. 15, pp. 1006-1009, Jul. 2015. Available: 10.1109/ISCAS.2015.7168806.
[7] X. Ding, B. Yan, Y. Zhang, J. Liu, et al., “Pulse Transit Time Based Continuous Cuffless Blood Pressure Estimation: A New Extension and A Comprehensive Evaluation,” Scientific Reports, vol. 7(1), pp. 1-11, Dec. 2017.
[8] Y. Liang, Z. Chen, R. Ward, “Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification,” Biosensors, vol. 8(4), pp. 1-13, Oct. 2018. Available: https://doi.org/10.3390/bios8040101
[9] F. Rundo, A. Orits, S. Battiato, S. Conoci, “Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis,” Computation, vol. 6(3), pp. 1-17, Aug. 2018. Available: https://doi.org/10.3390/computation6030046
[10] S. Shah, G. Gnanasegaran, J. Sunberg-Cohon, J. Buscombe, “The Heart: Anatomy, Physiology and Exercise Physiology,” Springer, Berlin, Heidelberg, 2009, pp. 3 -22.
[11] Anatomy and Physiology of the Heart
https://www.nottingham.ac.uk/nursing/practice/resources/cardiology/function/anatomy.php
[12] Handbook of Cardiac Anatomy, Physiology, and Devices, Humana Press, 2005, pp. 191-201.
[13] J. Parak, J. Havlik, “ECG Signal Processing and Heart Rate Frequency Detection Methods,” presented at the Technical Computing, Prague, Nov. 2011.
[14] M. Aqil, A. Jbari, “ECG Signal Denoising by Discrete Wavelet Transform,” International Journal of Online Engineering (iJOE), vol. 13, pp. 51-68, 2017. Available: https://doi.org/10.3991/ijoe.v13i09.7159
[15] Webster J G, “Design of Pulse Oximeters (Bristol: Institute of Physics Publishing),” Illustrated Ed, Bristol, 1997, pp. 1-244.
[16] V. Bhavana, M.J. Vidya, K.V. Padmaja, “Feasibility of Authenticating Medical Data Using Photoplethysmography(ppg) as Signature Mark,” The International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, pp. 635-341, Jan. 2014. Available: http://www.ijarcsse.com/
[17] A. Pachauri, M. Bhuyan, “ABP Peak Detection using Energy Analysis Technique,” presented at the International Conference on Multimedia, Signal Processing and Communication Technologies, Aligarh, India, Dec. 17-19, 2011.
[18] M. Schmidt, A. Schumann, K. Bär, G. Rose, “An automatic systolic peak detector of blood pressure waveforms using 4th order cumulants,” Current Directions in Biomedical Engineering, vol. 2(1), pp. 251–254, Sept. 2016.
[19] W. Lippincott, Wilkins, “Cardiovascular Physiology Concepts,” 2nd ed., Philadelphia, PA, 2012, pp:1-243.
[20] J. Parak, J. Havlik, “ECG Signal Processing and Heart Rate Frequency Detection Methods,” presented at the Technical Computing, Prague, Nov. 2011.
[21] https://commons.wikimedia.org/wiki/File:Ecg.png
[22] G.K. Sahoo, S. Ari, S.K. Patra, “ECG signal analysis for detection of Heart Rate and Ischemic Episodes,” presented at the International Journal of Advanced Computer Research, Thuckalay, Tamil Nadu, India, Apr. 11-12, 2013, vol. 3, pp. 148-152. Available: 10.1109/CICT.2013.6558254.
[23] Y. Zhang, C. Poon, C. Chan, et all., “A Health-Shirt using e-Textile Materials for the Continuous and Cuffless Monitoring of Arterial Blood Pressure,” presented at the 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors, Cambridge, MA, USA, Sept. 4-6, 2006, vol. 6, pp. 86-89. Available: 10.1109/ISSMDBS.2006.360104
[24] Y.C. Chiu., et al., “Determination of pulse wave velocities with computerized algorithms,” American Heart Journal, vol. 121(5), pp. 1460-1470, May. 1991. Available: https://doi.org/10.1016/0002-8703(91)90153-9
[25] B. Ibrahim,V. Nathan, R. Jafari, “Exploration and Validation of Alternate Sensing Methods for Wearable Continuous Pulse Transit Time Measurement Using Optical and Bioimpedance Modalities,” 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 17, pp. 2051-2055, Jul. 2017.
[26] Pan, Jiapu, and J. T. Willis, “A real-time QRS detection algorithm,” IEEE transactions on biomedical engineering, vol. BME-32, pp. 230-236, March. 1985.
[27] M. Elgendi, “On the Analysis of Fingertip Photoplethysmogram Signals,” Current Cardiology Reviews, vol. 8(1), pp. 14-25, Feb. 2012.
[28] L. Wang, E. Pickwell-MacPherson, Y.P Liang, Y.T Zhang, “Noninvasive cardiac output estimation using a novel photoplethysmogram index”, presented at the 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, Sept. 2-6, 2009. pp: 1746-1749.
[29] V.R. Ripoll, A. Vellido, “Blood Pressure Assessment with Differential Pulse Transit Time andDeep Learning: A Proof of Concept,” Kidney Diseases, vol. 5, pp. 23-27, 2019. Available: https://doi.org/10.1159/000493478
[30] C. Sammut, G. I. Webb, Encyclopedia of Machine Learning, Springer, Boston, MA, 2010.
[31] S. Chen, Z. Ji, H. Wu, Y. Xu, “A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning,” Sensors (Basel), vol. 19(11), pp. 1-18, Jun. 2019.
[32] S.G. Khalid, J. Zhang, F. Chen, D. Zheng, “Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches,” Journal of Healthcare Engineering, pp: 1-13, Oct. 2018. Available: https://doi.org/10.1155/2018/1548647
[33] Goodfellow, Y. Bengio, A. Courville, “Deep Learning,” in MIT Press, 2016. Available: http://www.deeplearningbook.org.
[34] S. Lee, J. Chang, “Oscillometric Blood Pressure Estimation Based on Deep Learning: A Proof of Concept,” IEEE Transactions on Industrial Informatics, vol. 13(2), pp. 461-472, Apr. 2017.
[35] B. Liang, Z. Chen, R. Ward, M. Elgendi, “Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification,” Biosensors, vol. 8(4), pp. 101-113, Dec. 2018.
[36] Burrus C.S, et al,. Fast Fourier Transforms, 2012.

[37] D. Donelly, B. Rust, “The Fast Fourier Transform for Experiments, Part I: Concepts,” Computing in Science and Engineering, vol. 7, pp. 80-88., March. 2005. Available: 10.1109/MCSE.2005.42
[38] A. Kobinal, G.K. Abledu, “Multiple Regression Analysis of the Impact of Senior Secondary School Certificate Examination (SSCE) Scores on the final Cumulative Grade Point Average (CGPA) of Students of Tertiary Institutions in Ghana,” Research on Humanities and Social Sciences, vol. 2, pp. 77-90, 2012.
[39] Ensemble methods: bagging, boosting and stacking.
https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205
[40] L. Breiman, “Random Forests,” in Machine Learning. vol. 45, pp. 5–32, 2001. Available: https://doi.org/10.1023/A:1010933404324
[41] Z. Cui, R. Ke, Y. Wang, “Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” International Workshop on Urban Computing (UrbComp), pp: 1-12, Jan. 2017.
[42] K. Greff, R.K. Srivastava, J. Koutnfk, et al., “LSTM: A Search Space Odyssey,” Transactions on Neural Networks and Learning System, vol. 28, pp. 1-2222-2232, Oct. 2017. Available: 10.1109/TNNLS.2016.2582924
[43] F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with lstm,” Proc. ICANN′99 Int. Conf. on Articial Neural Networks, Edinburgh, Scotland, vol. 2: pp. 850-855, 1999.
[44] V. Nair and G. Hinton, “Rectified linear units improve restricted boltzmann machines,” presented at the ICML′10 Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel, Jun. 21-24, 2010. pp. 807-814.
[45] K. He, X. Zhang, S. Ren, J. Sun, “Deep Residual Learning for Image Recognition,” presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 27-30, 2016. p. 770-778.
[46] P. Su, X. Ding, Y. Zhang, et al., “Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks,” pp. 323-328, Jan. 2018.
[47] MAE and RMSE — Which Metric is Better?
https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d
[48] Understanding the Bias-Variance Tradeoff
https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229
[49] M. Pankaj, W. Ching-Hao, G.R. Alexandre, and R. Clint, “A high-bias, low-variance introduction to Machine Learning for physicists,” Computational Physics, vol. 810, pp. 1-124, May. 2019.

指導教授

栗永徽(Yung-Hui Li)

審核日期

2019-8-1

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