近年來基於影像的非接觸式血壓量測研究，使用遠程光體積變化描記圖法 (remote Photoplethysmography, rPPG)取得訊號特徵後，透過機器學習或深度學習模型運算後可得到血壓估算數值。然而，現有的研究大多使用需要進行校正的脈波傳遞時間(Pulse Transit Time, PTT)或需要量測較長時間的心律變異度(Heart Rate Variability, HRV)這些特徵。因此，會需要較長的量測時間和定期校正。
本論文參考近期的非接觸式血壓量測研究，使用深度學習模型架構進行血壓估算，首先透過相機擷取臉部影像的訊號特徵，這些特徵可以分為兩組，一組為rPPG訊號波形的特徵與受試者的個人生理資料;另一組為rPPG的原始訊號與其一、二階導數。本論文避免使用PTT與HRV這類輸入特徵，以完成無需校正的快速非接觸式血壓量測系統。
本論文使用兩個資料集執行模型的訓練與驗證，資料集A執行五倍交叉驗證後所得的收縮壓平均絕對誤差(Mean Absolute Error, MAE)與誤差標準差(Standard Deviation of the Error, STD)分別為7.05 mmHg 和 7.25 mmHg，舒張壓則分別為6.74 mmHg 和 6.21 mmHg。而資料集B執行五倍交叉驗證後所得的收縮壓MAE與STD分別為7.30 mmHg 和 6.01 mmHg，舒張壓則分別為5.75 mmHg 和 4.56 mmHg。

In recent years, image-based non-contact blood pressure measurement research has utilized remote Photoplethysmography (rPPG) to extract signal features, which are then processed through machine learning or deep learning models to estimate blood pressure values. However, most existing studies rely on features like Pulse Transit Time (PTT), which requires calibration, or Heart Rate Variability (HRV), which necessitates longer measurement periods. As a result, these methods require longer measurement times and regular calibration.
This thesis draws on recent studies in non-contact blood pressure measurement and employs a deep-learning model architecture for blood pressure estimation. First, signal features are extracted from facial images captured by a camera. These features are divided into two groups: one group consists of rPPG waveform features combined with the subject's physiological data, and the other group comprises the raw rPPG signals and their first and second derivatives. This thesis avoids the use of input features like PTT and HRV, aiming to develop a rapid non-contact blood pressure measurement system without calibration.
In this study, the model was trained and validated using two datasets. For Dataset A, after performing a five-fold cross-validation, the Mean Absolute Error (MAE) and Standard Deviation of the Error (STD) for systolic blood pressure were 7.05 mmHg and 7.25 mmHg, respectively. In contrast, for diastolic blood pressure, the MAE and STD were 6.74 mmHg and 6.21 mmHg, respectively. For Dataset B, the MAE and STD for systolic blood pressure were 7.30 mmHg and 6.01 mmHg, respectively, while for diastolic blood pressure, the MAE and STD were 5.75 mmHg and 4.56 mmHg, respectively, after performing five-fold cross-validation.

[1] W. H. Organization, "Global Report on Hypertension: The Race Against a Silent Killer," 2023.
[2] S. S. Lim et al., "A Comparative Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990-2010: A Systematic Analysis for the Global Burden of Disease Study 2010," Lancet, vol. 380, no. 9859, pp. 2224-60, Dec 15 2012, doi: 10.1016/S0140-6736(12)61766-8.
[3] D. K. Arnett et al., "2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines," Circulation, vol. 140, no. 11, pp. e596-e646, Sep 10 2019, doi: 10.1161/CIR.0000000000000678.
[4] T. Unger et al., "2020 International Society of Hypertension Global Hypertension Practice Guidelines," Hypertension, vol. 75, no. 6, pp. 1334-1357, Jun 2020, doi: 10.1161/HYPERTENSIONAHA.120.15026.
[5] R. Mohamed, O. A. Omer, A.-M. M. Ali, and M. H. Aly, "Refining ROI Selection for Real-time Remote Photoplethysmography Using Adaptive Skin Detection," 2019 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE’2019), 2019.
[6] B.-F. Wu, B.-J. Wu, B.-R. Tsai, and C.-P. Hsu, "A Facial-Image-Based Blood Pressure Measurement System Without Calibration," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-13, 2022, doi: 10.1109/tim.2022.3165827.
[7] D. Qiao, A. H. Ayesha, F. Zulkernine, N. Jaffar, and R. Masroor, "ReViSe: Remote Vital Signs Measurement Using Smartphone Camera," IEEE Access, vol. 10, pp. 131656-131670, 2022, doi: 10.1109/access.2022.3229977.
[8] 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 Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 964-972, May 2016, doi: 10.1109/TBME.2015.2480679.
[9] S. Haddad, A. Boukhayma, and A. Caizzone, "Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2096-2105, May 2022, doi: 10.1109/JBHI.2021.3128229.
[10] M. Rong and K. Li, "A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning," Biomedical Signal Processing and Control, vol. 64, 2021, doi: 10.1016/j.bspc.2020.102328.
[11] Y. Z. Yoon et al., "Cuff-Less Blood Pressure Estimation Using Pulse Waveform Analysis and Pulse Arrival Time," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, pp. 1068-1074, Jul 2018, doi: 10.1109/JBHI.2017.2714674.
[12] Y. Zhou, H. Ni, Q. Zhang, and Q. Wu, "The Noninvasive Blood Pressure Measurement Based on Facial Images Processing," IEEE Sensors Journal, vol. 19, no. 22, pp. 10624-10634, 2019, doi: 10.1109/jsen.2019.2931775.
[13] K. Song, K.-y. Chung, and J.-H. Chang, "Cuffless Deep Learning-Based Blood Pressure Estimation for Smart Wristwatches," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4292-4302, 2020, doi: 10.1109/tim.2019.2947103.
[14] X. Fan, Q. Ye, X. Yang, and S. D. Choudhury, "Robust Blood Pressure Estimation Using an RGB Camera," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 11, pp. 4329-4336, 2018, doi: 10.1007/s12652-018-1026-6.
[15] M. Lewandowska, J. Rumiński, T. Kocejko, and J. Nowak, "Measuring Pulse Rate with a Webcam — A Non-contact Method for Evaluating Cardiac Activity," 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), 2011.
[16] P. V. Rouast, M. T. P. Adam, R. Chiong, D. Cornforth, and E. Lux, "Remote Heart Rate Measurement Using Low-cost RGB Face Video: A Technical Literature Review," Frontiers of Computer Science, vol. 12, no. 5, pp. 858-872, 2018, doi: 10.1007/s11704-016-6243-6.
[17] R. Song, S. Zhang, C. Li, Y. Zhang, J. Cheng, and X. Chen, "Heart Rate Estimation From Facial Videos Using a Spatiotemporal Representation With Convolutional Neural Networks," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 7411-7421, 2020, doi: 10.1109/tim.2020.2984168.
[18] J. Peng, W. Su, Z. Tian, D. Zang, X. Li, and Z. Song, "MVPD: A Multimodal Video Physiology Database for rPPG," 2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI), pp. 173-177, 2023, doi: 10.1109/seai59139.2023.10217565.
[19] F. Schrumpf, P. Frenzel, C. Aust, G. Osterhoff, and M. Fuchs, "Assessment of Deep Learning based Blood Pressure Prediction from PPG and rPPG signals," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3815-3825, 2021, doi: 10.1109/cvprw53098.2021.00423.
[20] R. Hommes, C. Van Pul, P. Andriessen, and E. Cottaar, "Measuring Vital Signs and Simulating Hemodynamic Patterns During Closure of the Patent Ductus Arteriosus," 2015.
[21] I. Ilhan, "Development of Blood Pressure Measurement Device Using The Curve Fitting Method," International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 3, pp. 145-152, 2019, doi: 10.18201/ijisae.2019355376.
[22] Ü. ŞEntÜRk, K. Polat, and İ. YÜCedaĞ, "Towards Wearable Blood Pressure Measurement Systems from Biosignals: a Review," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, no. 5, pp. 3259-3281, 2019, doi: 10.3906/elk-1812-121.
[23] Celka, Patrick, Granqvist, Niclas, Schwabl, and Herbert, "Traditional Tibetan Pulse Reading in the Digital Era," Sowa Rigpa Journal, 2019.
[24] A. Galli, R. J. H. Montree, S. Que, E. Peri, and R. Vullings, "An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications," Sensors (Basel), vol. 22, no. 11, May 26 2022, doi: 10.3390/s22114035.
[25] X. He, R. A. Goubran, and X. P. Liu, "Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement," IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 6, pp. 1431-1439, 2014, doi: 10.1109/tim.2014.2299524.
[26] R. Shriram, A. Wakankar, N. Daimiwal, and D. Ramdasi, "Continuous Cuffless Blood Pressure Monitoring based on PTT," 2010 International Conference on Bioinformatics and Biomedical Technology, 2010.
[27] G. Fortino and V. Giampà, "PPG-based Methods for Non-Invasive and Continuous Blood Pressure Measurement: an Overview and Development Issues in Body Sensor Networks," 2010 IEEE International Workshop on Medical Measurements and Applications, 2010.
[28] M. Y. Wong, C. C. Poon, and Y. T. Zhang, "An Evaluation of the Cuffless Blood Pressure Estimation based on Pulse Transit Time Technique: a Half Year Study on Normotensive Subjects," Cardiovascular Engineering, vol. 9, no. 1, pp. 32-8, Mar 2009, doi: 10.1007/s10558-009-9070-7.
[29] W. Verkruysse, L. O. Svaasand, and J. S. Nelson, "Remote Plethysmographic Imaging Using Ambient Light," Optics Express, 2008.
[30] M. Z. Poh, D. J. McDuff, and R. W. Picard, "Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam," IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 7-11, Jan 2011, doi: 10.1109/TBME.2010.2086456.
[31] Y. Sun, C. Papin, V. Azorin-Peris, R. Kalawsky, S. Greenwald, and S. Hu, "Use of Ambient Light in Remote Photoplethysmographic Systems: Comparison between a High-Performance Camera and a Low-cost Webcam," Journal of Biomedical Optics, vol. 17, no. 3, p. 037005, Mar 2012, doi: 10.1117/1.JBO.17.3.037005.
[32] K. Murakami, M. Yoshioka, and J. Ozawa, "Non-contact Pulse Transit Time Measurement Using Imaging Camera, and its Relation to Blood Pressure," 2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015.
[33] S. Kwon, J. Kim, D. Lee, and K. Park, "ROI Analysis for Remote Photoplethysmography on Facial Video," presented at the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
[34] H. Luo et al., "Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology," Circ Cardiovasc Imaging, vol. 12, no. 8, p. e008857, Aug 2019, doi: 10.1161/CIRCIMAGING.119.008857.
[35] G. Slapnicar, N. Mlakar, and M. Lustrek, "Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network," Sensors (Basel), vol. 19, no. 15, Aug 4 2019, doi: 10.3390/s19153420.
[36] L. Kong et al., "Non-contact Detection of Oxygen Saturation based on Visible Light Imaging Device Using Ambient Light," Optics Express, vol. 21, no. 15, pp. 17464-71, Jul 29 2013, doi: 10.1364/OE.21.017464.
[37] M. Chen, Q. Zhu, H. Zhang, M. Wu, and Q. Wang, "Respiratory Rate Estimation from Face Videos," IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019.
[38] H. Komajou. "Mediapipe landmark face/hand/pose sequence number list view." https://medium.com/@hotakoma/mediapipe-landmark-face-hand-pose-sequence-number-list-view-778364d6c414. (accessed September,3, 2023).
[39] S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia, "Human Skin Detection Using RGB, HSV and YCbCr Color Models," Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016), 2016.
[40] A. R. Smith, "Color gamut transform pairs," ACM SIGGRAPH Computer Graphics, pp. 12-19, 1978, doi: 10.1145/800248.807361.
[41] W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, "Algorithmic Principles of Remote PPG," IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1479-1491, Jul 2017, doi: 10.1109/TBME.2016.2609282.
[42] X. Han, X. Yang, S. Fang, R. Song, L. Li, and J. Zhang, "Noncontact Blood Pressure Estimation Using BP-Related Cardiovascular Knowledge: An Uncalibrated Method Based on Consumer-Level Camera," IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, doi: 10.1109/tim.2023.3279448.
[43] V. M. Musini and J. M. Wright, "Factors Affecting Blood Pressure Variability: Lessons Learned from Two Systematic Reviews of Randomized Controlled Trials," PLoS One, vol. 4, no. 5, p. e5673, May 22 2009, doi: 10.1371/journal.pone.0005673.
[44] Y. Imai et al., "Factors that Affect Blood Pressure Variability. A Community-based Study in Ohasama, Japan," American Journal of Hypertension, 1997.
[45] X. Ding, W. Wang, Y. Chen, Y. Yang, Y. Zhao, and D. Kong, "Feasibility Study of Pulse Width at Half Amplitude of Camera PPG for Contactless Blood Pressure Estimation," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), vol. 2021, pp. 365-368, Nov 2021, doi: 10.1109/EMBC46164.2021.9630964.
[46] S. Reule and P. E. Drawz, "Heart Rate and Blood Pressure: Any Possible Implications for Management of Hypertension?," Current Hypertension Reports, vol. 14, no. 6, pp. 478-84, Dec 2012, doi: 10.1007/s11906-012-0306-3.
[47] E. B. Schroeder, D. Liao, L. E. Chambless, R. J. Prineas, G. W. Evans, and G. Heiss, "Hypertension, Blood Pressure, and Heart Rate Variability: the Atherosclerosis Risk in Communities (ARIC) study," Hypertension, vol. 42, no. 6, pp. 1106-11, Dec 2003, doi: 10.1161/01.HYP.0000100444.71069.73.
[48] M. Elgendi et al., "Frequency Analysis of Photoplethysmogram and its Derivatives," Comput Methods Programs Biomed, vol. 122, no. 3, pp. 503-12, Dec 2015, doi: 10.1016/j.cmpb.2015.09.021.
[49] M. Elgendi, Y. Liang, and R. Ward, "Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms," Diseases, vol. 6, no. 1, Mar 11 2018, doi: 10.3390/diseases6010020.
[50] B.-J. Wu, B.-F. Wu, and C.-P. Hsu, "Camera-Based Blood Pressure Estimation via Windkessel Model and Waveform Features," IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, doi: 10.1109/tim.2022.3224534.
[51] 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," Journal of Hypertension, 1990.