臺灣博碩士論文加值系統

本論文旨在設計一套非侵入之耳機式連續血壓監測系統，提出於人體耳甲腔區域放置光學生物訊號感測器以獲得光體積變化描記圖（Photoplethysmography, PPG）訊號藉以計算收縮壓與舒張壓，再於智慧型裝置呈現即時的生理監測數值並記錄使用者長時間的生理參數。耳朵部位生理監測裝置與常見腕錶式相比，置於耳內的感測器不易受外在環境及光源影響，且耳部具有豐富微血管及軟骨一為反射式PPG最佳的反射區域，頭部也是人體平衡重要之處因此相對穩定，將大大的提升PPG數據品質，傳統式袖帶式血壓計因儀器較龐大需要在固定地點量測，且受限於儀器量測方法無法連續測量血壓，因此本論文提出了脈波傳導時間(Pulse Transit Time, PTT)結合機器學習之單一PPG非侵入式血壓量測系統，無需使用心電圖（Electrocardiography, ECG）僅透過單一PPG即可有效地估算血壓，並且取得光體積變化描記圖時域波形特徵與二階導數血管特徵，結合改良後的血流動力學公式與機器學習訓練血壓模型，此演算法架構可確保此監測裝置系統可精準計算生理參數之目標。受測者進行實驗收案，使用醫療級24小時動態血壓計(Oscar 2)作為驗證標準，靜態實驗結果顯示血壓平均皮爾森相關係數r大於0.9強相關性，舒張壓平均誤差(MAE±SD)為2.04±3.33(mmHg)，AAMI中達到了最高準確度的類別（最高準確度），而在英國高血壓協會(BHS)屬於A級（最高等級），收縮壓平均誤差為4.65±7.39(mmHg)，在英國高血壓協會(BHS)屬於B級，此方法在未來追蹤人體血壓是非常有幫助的。

This purpose aimed at designing a non-invasive earphone-type continuous blood pressure monitoring system, and it puts a biomedical sensor photoplethysmography (PPG) in the region of the human ear cavity to calculate the systolic and diastolic pressure. The smart device presents a real-time physiological monitoring platform to display and record the user's long-term physiological parameters. The ear has rich microvessels and cartilage as the best reflection area of the reflective PPG. Compared with the common wrist watch type, sensors placed in the ear are also less susceptible to environmental light sources and the head is also important for human balance. Therefore, it is relatively stable and will greatly improve the quality of PPG data.This purpose proposes a single PPG non-invasive blood pressure measurement system combining Pulse Transit Time (PTT) and machine learning, which can effectively estimate blood pressure without using electrocardiography (ECG). Calculation of blood vessel characteristics of the second derivative photoplethysmogram (SDPPG) change tracing diagram. The algorithm architecture can ensure that the monitoring device system can accurately calculate the target of physiological parameters. A total of male and female subjects accepted the experiment and used a medical-grade 24-hour ambulatory sphygmomanometer (Oscar 2) as the verification standard.The static experiment results showed that the blood pressure averaged Pearson correlation coefficient r is greater than the strong correlation of 0.85.The mean error of diastolic blood pressure concentration is 2.04±3.33 (mmHg) and the mean error of systolic blood pressure concentration is 4.65±7.39 (mmHg). According to the BHS standard, the proposed method is consistent with the grade A in the estimation of DBP and with the grade B in the estimation of the SBP.

摘要 i
ABSTRACT iii
誌謝 v
目錄 vi
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 文獻回顧 3
1.4 論文貢獻 10
1.5 論文架構 10
第二章 研究背景及原理 11
2.1 耳部生理結構探討 11
2.1.1 外耳結構 12
2.2 光體積變化描記圖概述 13
2.2.1 光體積變化描記圖原理 14
2.2.2 光體積變化描記圖波型 16
2.2.3 光體積變化描記圖量測方式 17
2.2.4 光體積變化描記圖特徵成分 19
2.2.5 光體積變化描記圖應用 24
2.3 機器學習 34
2.3.1 長短期記憶（Long Short-Term Memory，LSTM） 34
第三章 系統演算法與架構 38
3.1 系統簡介 38
3.2 資料庫血壓訓練 39
3.2.1 資料庫來源 39
3.2.2 機器學習之資料處理 40
3.2.3 資料前處理 41
3.2.4 PPG特徵提取 44
3.2.5 PPG特徵點檢查 45
3.2.6 機器學習 46
3.3 血壓演算法 48
3.3.1 帶通濾波 49
3.3.2 正規化 49
3.3.3 二次導數光體積變化描記圖 50
3.3.4 動態範圍檢查 50
3.3.5 PPG特徵提取 51
3.3.6 PPG特徵點檢查 52
3.4 系統硬體整合設計 53
3.4.1 裝置組成元件 53
3.4.2 裝置外觀 58
3.4.3 監測平台APP設計 59
第四章 實驗結果與討論 60
4.1 血流動力學血壓預測 60
4.2 AI血壓模型預測 66
4.3 執行時間分析 69
4.4 預測血壓文獻比較 70
4.5 人體靜態實驗 71
第五章 結論與未來展望 74
5.1 結論 74
5.2 未來展望 74
參考文獻 75

[1]重陽節衛生福利統計通報，[Online]. Available: https://www.mohw.gov.tw/dl-56528-b9606326-dcc2-4421-8208-339a250fd222.html.
[2]高血壓不是中老年人專利， [Online]. Available: https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=1136&pid=3106
[3]Mitchell GF, Parise H, Benjamin EJ, et al. Changes in arterial stiffness and wave reflection with advancing age in healthy men and women: the Framingham Heart Study. Hypertension. 2004;43(6), pp. 1239-1245.
[4]M. Kachuee et al., "Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time", in Proc. IEEE Int. Symp. Circuits Syst., pp. 1006-1009, May. 2015.
[5]T. Pereira, C. Correia and J. Cardoso, "Novel methods for pulse wave velocity measurement", J. Med. Biol. Eng., vol. 35, no. 5, pp. 555-565, Oct. 2015.
[6]A. Sahoo, P. Manimegalai and K. Thanushkodi, "Wavelet based pulse rate and blood pressure estimation system from ECG and PPG signals", in Proc. IEEE Int. Conf. Comput. Commun. Elect. Technol. (ICCCET), pp. 285-289, Mar. 2011.
[7]Y. Yoon et al., "Non-constrained blood pressure monitoring using ECG and PPG for personal healthcare", J. Med. Syst., vol. 33, pp. 261-266, 2009.
[8]D. B. McCombie, A. T. Reisner and H. H. Asada, "Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics", in Proc. 28th Ann. Int. Conf. IEEE EMBS, pp. 3521-3524, 2006-Aug.-Sep.-30–3.
[9]X. F. Teng and Y. T. Zhang, "Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach", Proc. 2003 25th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 4, pp. 3153-3156, 2003.
[10]Yu. Kurylyak, F. Lamonaca and D. Grimaldi, "A neural network-based method for continuous blood pressure estimation from a PPG signal", Proc. 2013 IEEE International Instrumentation and Measurement Technology Conf., pp. 280-283, 2013.
[11]A. Gaurav et al., "Cuff-less PPG based continuous blood pressure monitoring – A smartphone based approach", Proc. 38th Annu Int. IEEE Eng. Med. Biol. Soc. Conf., pp. 607-610, Aug. 2016.
[12]W. B. Gu, C. C. Y. Poon and Y. T. Zhang, "A novel parameter from PPG dicrotic notch for estimation of systolic blood pressure using pulse transit time", Proc. ISSS-MDBS, pp. 86-88, Jun. 2008.
[13]SC Millasseau, RP Kelly, JM Ritter and PJ. Chowienczyk, "Determination of age-related increases in large artery stiffness by digital pulse contour analysis", Clin.Sci.(Lond) 2002, vol. 103, no. 4, pp. 371-377.
[14]D. N. Dutt and S. Shruthi, "Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment" ,in Proc., 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 1506-1510, 2015.
[15]D. McDuf et al., "Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera", IEEE Trans. Biomed. Eng., vol. 61, no. 12, pp. 2948-2954, Dec. 2014.
[16]耳朵結構解剖平面圖，[Online]. Available:http://focus.uho.com.tw/hearinghealth/sound_discover_detail.asp?id=379 
[17]盧柏安。「耳機式生理監測系統」。碩士論文，國立臺北科技大學電子工程系，2018。
[18]耳部耳廓結構，[Online]. Available: https://baike.baidu.com/item/%E5%A4%96%E8%80%B3
[19]人體耳部血液供應，[Online]. Available: http://yibian.hopto.org/shu/?sid=8190
[20]人體耳部血管分布圖，[Online]. Available: https://plasticsurgerykey.com/ear/
[21]光學生物感測器，[Online]. Available:https://www.osram.com/os/ecat/SFH%207050/com/en/class_pim_web_catalog_103489/global/prd_pim_device_2220012/
[22]Y.-H. Kao, P. C.-P. Chao and C.-L. Wey, "Design and Validation of a New PPG Module to Acquire High-Quality Physiological Signals for High-Accuracy Biomedical Sensing", IEEE J. Sel. Top. Quantum Electron., vol. 25, no. 1, pp. 1-10, Jan. 2019.
[23] T. Tamura, Y. Maeda, M. Sekine and M. Yoshida, "Wearable photoplethysmographic sensors—Past and present", Electronics, vol. 3, no. 2, pp. 282-302, 2014.
[24]林育德. 脈波信號與ppg 信號特徵之相關性研究. 中醫藥年報, Volume 28(1): 361–388, 2010.
[25]Y. Li, H. Gu, H. Fok, J. Alastruey, P. Chowienczyk Forward and backward pressure waveform morphology in hypertension Hypertension, 69 (2017), pp. 375-381
[26]X.-R. Ding, Y.-T. Zhang, J. Liu, W.-X. Dai and H. K. Tsang, "Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio", IEEE Trans. Biomed. Eng., vol. 63, no. 5, pp. 964-972, May 2016.
[27]M. Elgendi, R. Fletcher, Y. Liang, N. Howard, N. H. Lovell, D. Abbott, et al., "The use of photoplethysmography for assessing hypertension", NPJ Digit. Med., vol. 2, no. 1, pp. 60, Dec. 2019.
[28] X.-R. Ding, Y.-T. Zhang, J. Liu, W.-X. Dai and H. K. Tsang, "Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio", IEEE Trans. Biomed. Eng., vol. 63, no. 5, pp. 964-972, May 2016
[29]M. Elgendi, "On the analysis of fingertip photoplethysmogram signals", Current Cardiol. Rev., vol. 8, no. 1, pp. 14-25, 2012.
[30]K. Takazawa, N. Tanaka, M. Fujita, O. Matsuoka, T. Saiki, M. Aikawa, et al., "Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform", Hypertension, vol. 32, no. 2, pp. 365-370, 1998.
[31]M. Raghuram, K. Sivani and K. Ashoka Reddy, "Reduction of motion artifacts from pulse oximeter’s PPG signals using MSICA", in Proc., Int. Conf. Control Instrumentation Communication and Computational Technologies (ICCICCT), pp. 491-494, Dec. 2016.
[32]T. Y. Abay and P. A. Kyriacou, "Reflectance photoplethysmography as noninvasive monitoring of tissue blood perfusion", IEEE Trans. Biomed. Eng., vol. 62, no. 9, pp. 2187-2195, Sep. 2015.
[33]H. Gesche et al., "Continuous blood pressure measurement by using the pulse transit time: Comparison to a cuff-based method", Eur. J. Appl. Physiol., vol. 112, pp. 309-315, 2012.
[34]H. A. Nygaard, "Measuring body mass index (bmi) in nursing home residents: The usefulness of measurement of arm span", Scandinavian J. Primary Health Care, vol. 26, no. 1, pp. 46-49, 2008.
[35]Bramwell J. C. & Hill A. V., "The Velocity of the Pulse Wave in Man", Proc. Royal Society for experim. Biology & Med., Vol. 93, pp. 298-306, London, 1922
[36]D. J. Korteweg, "Uber die Fortpflanzungsgeschwindigkeit des Schalles in elastischen Röhren", Ann. d. Physik. III, pp. 525-542, 1878.
[37]T. Secomb, "Hemodynamics", Comprehensive Physiol., vol. 6, no. 2, pp. 975-1003, 2016.
[38]D. J. Hughes et al., "Measurements of young's modulus of elasticity of the canine aorta with ultrasound", Ultrasonic Imag., vol. 1, pp. 356-367, 1979.
[39]A. J. Bank, R. F. Wilson, S. H. Kubo, J. E. Holte, T. J. Dresing and H. Wang, "Direct effects of smooth muscle relaxation and contraction on in vivo human brachial artery elastic properties", Circ. Res., vol. 77, no. 5, pp. 1008-1016, Nov. 1995.
[40]A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., "PhysioBank PhysioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals", Circulation, vol. 101, no. 23, pp. e215-e220, Jun. 2000.
[41]LSTM_深度學習_股價預測，[Online]. Available: https://medium.com/data-scientists-playground/lstm-%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-%E8%82%A1%E5%83%B9%E9%A0%90%E6%B8%AC-cd72af64413a
[42]Understanding LSTM Networks, [Online]. Available: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
[43]Kass DA, Chen CH, Nevo E, Fetics B, Pak PH, Maughan WL, Yin FCP. Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure data. Circulation. 1998; 98: 186–187.Letter.
[44]SFH7050反射式光學生物感測器，[Online]. Available: https://www.mouser.tw/ProductDetail/OSRAM-Opto-Semiconductors/SFH-7050?qs=beZBGHSk9c35Nini1YeCrg%3D%3D
[45]CC2650MODA微控制器模組，[Online]. Available: https://www.mgsuperlabs.co.in/estore/TI-SimpleLink-Bluetooth-low-energy-CC2650-Module-BoosterPack-Plug-in-Module
[46]CC2650MODA微控制器架構，[Online]. Available: http://www.ti.com/product/CC2650?keyMatch=CC2650&tisearch=Search-EN-Everything
[47]M. Kachuee, M. M. Kiani, H. Mohammadzade and M. Shabany, "Cuffless blood pressure estimation algorithms for continuous health-care monitoring", IEEE Trans. Biomed. Eng., vol. 64, no. 4, pp. 859-869, Apr. 2017.
[48]E. O. Brien et al., “The british hypertension society protocol for the evaluation of automated and semi-automated blood pressure measuring devices with special reference to ambulatory systems.” J. Hypertens., vol. 8, no. 7, pp. 607–619, 1990.