A multi-source information fusion-based method for the local pulse wave velocity estimation in carotid artery_Journal of Biomedical Engineering

Authors：

CHEN Zhi ¹ ,  DENG Li ^1,2 , ZHANG Yufeng ² , XIA Dawen ^3,1 , SONG Wenxia ¹ , LU Youjun ¹ , WANG Xin ¹ , AN Yi ¹

1. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, P. R. China;
2. Department of Electronic Engineering, Information School, Yunnan University, Kunming 650000, P. R. China;
3. Engineering Research Center for Micro Nano and Intelligent Manufacturing, Ministry of Education, Kaili College, Kaili, Guizhou 556011, P. R. China;

Corresponding?author：

DENG Li, Email: ldeng0726@gzmu.edu.cn

Keywords：

Local pulse wave velocity; Time-domain feature; Carotid artery; Machine learning

DOI：

10.7507/1001-5515.202502066

Video：

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Aiming at the limitations of low accuracy and poor stability in the transit time method for estimating carotid local pulse wave velocity, this paper proposed a machine learning-based local pulse wave velocity estimation method, which integrated carotid pulse wave time-domain features, age, and cardiac function parameters. The research was based on a dataset of carotid pulse wave propagation from 4 374 virtual subjects. By combining the Pearson correlation coefficient method and the least absolute shrinkage and selection algorithm to select multi-position combinations of pulse wave time-domain features, and integrating age, heart rate, and other parameters as input features, five machine learning models including multiple linear regression, Bayesian ridge regression, k-nearest neighbor regression, support vector regression and convolutional neural network were used to construct the carotid local pulse wave velocity estimation model, respectively. The results demonstrated that all five machine learning models showed higher accuracy and stronger stability than the traditional methods, and the support vector regression model achieved the optimal performance, with a normalized root mean square error of less than 1.80% and a coefficient of determination exceeding 0.980. In conclusion, it is hoped that the research results presented in this paper can provide a theoretical basis and technical support for the early quantitative assessment of local vascular elasticity of the carotid artery in clinic.

Citation： CHEN Zhi, DENG Li, ZHANG Yufeng, XIA Dawen, SONG Wenxia, LU Youjun, WANG Xin, AN Yi. A multi-source information fusion-based method for the local pulse wave velocity estimation in carotid artery. Journal of Biomedical Engineering, 2026, 43(1): 123-130. doi: 10.7507/1001-5515.202502066 Copy

1.	World Heart Federation. World Heart Report 2023: confronting the world’s number one killer. Geneva, Switzerland: World Heart Federation, 2023.
2.	楊瑤, 劉劍雄, 張蕓, 等. 大動脈粥樣硬化性腦卒中與心臟功能關系的臨床研究. 昆明醫科大學學報, 2022, 43(11): 107-111.
3.	Frostegard J. Systemic lupus erythematosus and cardiovascular disease. Journal of Internal Medicine, 2023, 293(1): 48-62.
4.	姚瑞晗, 張榆鋒, 施繼紅, 等. 頸動脈斑塊的超聲仿真及系統實現. 生物醫學工程學雜志, 2016, 33(6): 1095-1102.
5.	Pilz N, Heinz V, Ax T, et al. Pulse wave velocity: methodology, clinical applications, and interplay with heart rate variability. Reviews in Cardiovascular Medicine, 2024, 25(7): 266.
6.	劉寶華, 任曉華. 脈搏波傳導速度測量算法的研究及其進展. 生物醫學工程學雜志, 2010, 27(1): 231-235.
7.	Zhong Q, Hu M J, Cui Y J, et al. Carotid–femoral pulse wave velocity in the prediction of cardiovascular events and mortality: an updated systematic review and meta-analysis. Angiology, 2018, 69(7): 617-629.
8.	Nabeel P M, Kiran V R, Joseph J, et al. Local pulse wave velocity: theory, methods, advancements, and clinical applications. IEEE Reviews in Biomedical Engineering, 2020, 13: 74-112.
9.	Manoj R, Kiran V R, Ponkalaivani S, et al. Measurement of local pulse wave velocity: agreement among various methodologies//2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Jeju Island: IEEE, 2023: 1-6.
10.	鄧麗, 張榆鋒, 楊麗春, 等. 超聲傳輸時間法頸動脈脈搏波速估計精度及影響因素研究. 電子與信息學報, 2017, 39(2): 316-321.
11.	鄧麗. 超聲傳輸時間法的人體頸動脈局域脈搏波速估計. 昆明: 云南大學, 2019.
12.	Deng L, Zhang Y, Chen Z, et al. Regional upstroke tracking for transit time detection to improve the ultrasound-based local PWV estimation in carotid arteries. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020, 67(4): 691-702.
13.	Deng L, Zhang Y, Mo H. Evaluation of TT-based local PWV estimation for different propagation velocities//Proceedings of the 2018 5th International Conference on Biomedical and Bioinformatics Engineering (ICBBE). Okinawa: HKCBEES, 2018: 42-46.
14.	Jin W, Chowienczyk P, Alastruey J. Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms. PloS One, 2021, 16(6): e0245026.
15.	Tavallali P, Razavi M, Pahlevan N M. Artificial intelligence estimation of carotid-femoral pulse wave velocity using carotid waveform. Scientific Reports, 2018, 8(1): 1014.
16.	Garcia J M V, Bahloul M A, Laleg-Kirati T M. A multiple linear regression model for carotid-to-femoral pulse wave velocity estimation based on schrodinger spectrum characterization. Annu Int Conf IEEE Eng Med Biol Soc, 2022: 143-147.
17.	Bahloul M A, Chahid A, Laleg-Kirati T M. A multilayer perceptron-based carotid-to-femoral pulse wave velocity estimation using ppg signal//2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Athens: IEEE, 2021: 1-6.
18.	Abrisham K P, Alipour K, Tarvirdizadeh B, et al. Advancing PPG-based cf-PWV estimation with an integrated CNN-BiLSTM-Attention model. Signal, Image and Video Processing, 2024, 18: 8621-8633.
19.	Charlton P H, Mariscal H J, Vennin S, et al. Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexe. American Journal of Physiology-Heart and Circulatory Physiology, 2019, 317(5): H1062-H1085.
20.	Alastruey J, Charlton P H, Bikia V, et al. Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet. American Journal of Physiology-Heart and Circulatory Physiology, 2023, 325(1): H1-H29.
21.	Vargas J M, Bahloul M A, Laleg-Kirati T M. A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: an in silico study. Frontiers in Physiology, 2023, 14: 1100570.
22.	Manoj R, Raj K V, Nabeel P M, et al. Arterial pressure pulse wave separation analysis using a multi-Gaussian decomposition model. Physiological Measurement, 2022, 43(5): 055005.
23.	Charlton P H, Celka P, Farukh B, et al. Assessing mental stress from the photoplethysmogram: a numerical study. Physiological Measurement, 2018, 39(5): 054001.
24.	Maqsood S, Xu S, Springer M, et al. A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG). IEEE Access, 2021, 9: 138817-138833.
25.	Mukaka M M. A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 2012, 24(3): 69-71.
26.	Liu Y, Sun L, Du C, et al. Near-infrared prediction of edible oil frying times based on Bayesian ridge regression. Optik, 2020, 218: 164950.
27.	Halder R K, Uddin M N, Uddin M A, et al. Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. Journal of Big Data, 2024, 11(1): 113.
28.	Vedaldi A, Zisserman A. VGG convolutional neural networks practical. Department of Engineering Science, University of Oxford, 2017: 66.
29.	Yin L X, Ma C Y, Wang S, et al. Reference values of carotid ultrafast pulse-wave velocity: a prospective, multicenter, population-based study. Journal of the American Society of Echocardiography, 2021, 34(6): 629-641.
30.	Gu O, He B, Xiong L, et al. Reconstructive interpolation for pulse wave estimation to improve local PWV measurement of carotid artery. Med Biol Eng Comput, 2024, 62(5): 1459-1473.
31.	Mo H, Lang X, Zhang Y, et al. Optimally filtering and matching processing for regional upstrokes to improve ultrasound transit time-based local PWV estimation. Computer Methods and Programs in Biomedicine, 2022, 224: 106997.
32.	肖建, 于龍, 白裔峰. 支持向量回歸中核函數和超參數選擇方法綜述. 西南交通大學學報, 2008, 21(3): 297-303.
33.	Salih A M, Raisi E Z, Galazzo I B, et al. A perspective on explainable artificial intelligence methods: SHAP and LIME. Advanced Intelligent Systems, 2025, 7(1): 2400304.
34.	Wei C C. Developing an effective arterial stiffness monitoring system using the spring constant method and photoplethysmography. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 151-154.
35.	Ahn J M. New aging index using signal features of both photoplethysmograms and acceleration plethysmograms. Healthcare Informatics Research, 2017, 23(1): 53-59.
36.	Morris D C. Clinical methods: the history, physical, and laboratory examinations. 3rd edition. Boston: Butterworths,1990: 112.
37.	Schober P, Boer C, Schwarte L A. Correlation coefficients: appropriate use and interpretation. Anesth Analg, 2018, 126(5): 1763-1768.

1. World Heart Federation. World Heart Report 2023: confronting the world’s number one killer. Geneva, Switzerland: World Heart Federation, 2023.
2. 楊瑤, 劉劍雄, 張蕓, 等. 大動脈粥樣硬化性腦卒中與心臟功能關系的臨床研究. 昆明醫科大學學報, 2022, 43(11): 107-111.
3. Frostegard J. Systemic lupus erythematosus and cardiovascular disease. Journal of Internal Medicine, 2023, 293(1): 48-62.
4. 姚瑞晗, 張榆鋒, 施繼紅, 等. 頸動脈斑塊的超聲仿真及系統實現. 生物醫學工程學雜志, 2016, 33(6): 1095-1102.
5. Pilz N, Heinz V, Ax T, et al. Pulse wave velocity: methodology, clinical applications, and interplay with heart rate variability. Reviews in Cardiovascular Medicine, 2024, 25(7): 266.
6. 劉寶華, 任曉華. 脈搏波傳導速度測量算法的研究及其進展. 生物醫學工程學雜志, 2010, 27(1): 231-235.
7. Zhong Q, Hu M J, Cui Y J, et al. Carotid–femoral pulse wave velocity in the prediction of cardiovascular events and mortality: an updated systematic review and meta-analysis. Angiology, 2018, 69(7): 617-629.
8. Nabeel P M, Kiran V R, Joseph J, et al. Local pulse wave velocity: theory, methods, advancements, and clinical applications. IEEE Reviews in Biomedical Engineering, 2020, 13: 74-112.
9. Manoj R, Kiran V R, Ponkalaivani S, et al. Measurement of local pulse wave velocity: agreement among various methodologies//2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Jeju Island: IEEE, 2023: 1-6.
10. 鄧麗, 張榆鋒, 楊麗春, 等. 超聲傳輸時間法頸動脈脈搏波速估計精度及影響因素研究. 電子與信息學報, 2017, 39(2): 316-321.
11. 鄧麗. 超聲傳輸時間法的人體頸動脈局域脈搏波速估計. 昆明: 云南大學, 2019.
12. Deng L, Zhang Y, Chen Z, et al. Regional upstroke tracking for transit time detection to improve the ultrasound-based local PWV estimation in carotid arteries. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020, 67(4): 691-702.
13. Deng L, Zhang Y, Mo H. Evaluation of TT-based local PWV estimation for different propagation velocities//Proceedings of the 2018 5th International Conference on Biomedical and Bioinformatics Engineering (ICBBE). Okinawa: HKCBEES, 2018: 42-46.
14. Jin W, Chowienczyk P, Alastruey J. Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms. PloS One, 2021, 16(6): e0245026.
15. Tavallali P, Razavi M, Pahlevan N M. Artificial intelligence estimation of carotid-femoral pulse wave velocity using carotid waveform. Scientific Reports, 2018, 8(1): 1014.
16. Garcia J M V, Bahloul M A, Laleg-Kirati T M. A multiple linear regression model for carotid-to-femoral pulse wave velocity estimation based on schrodinger spectrum characterization. Annu Int Conf IEEE Eng Med Biol Soc, 2022: 143-147.
17. Bahloul M A, Chahid A, Laleg-Kirati T M. A multilayer perceptron-based carotid-to-femoral pulse wave velocity estimation using ppg signal//2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Athens: IEEE, 2021: 1-6.
18. Abrisham K P, Alipour K, Tarvirdizadeh B, et al. Advancing PPG-based cf-PWV estimation with an integrated CNN-BiLSTM-Attention model. Signal, Image and Video Processing, 2024, 18: 8621-8633.
19. Charlton P H, Mariscal H J, Vennin S, et al. Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexe. American Journal of Physiology-Heart and Circulatory Physiology, 2019, 317(5): H1062-H1085.
20. Alastruey J, Charlton P H, Bikia V, et al. Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet. American Journal of Physiology-Heart and Circulatory Physiology, 2023, 325(1): H1-H29.
21. Vargas J M, Bahloul M A, Laleg-Kirati T M. A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: an in silico study. Frontiers in Physiology, 2023, 14: 1100570.
22. Manoj R, Raj K V, Nabeel P M, et al. Arterial pressure pulse wave separation analysis using a multi-Gaussian decomposition model. Physiological Measurement, 2022, 43(5): 055005.
23. Charlton P H, Celka P, Farukh B, et al. Assessing mental stress from the photoplethysmogram: a numerical study. Physiological Measurement, 2018, 39(5): 054001.
24. Maqsood S, Xu S, Springer M, et al. A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG). IEEE Access, 2021, 9: 138817-138833.
25. Mukaka M M. A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 2012, 24(3): 69-71.
26. Liu Y, Sun L, Du C, et al. Near-infrared prediction of edible oil frying times based on Bayesian ridge regression. Optik, 2020, 218: 164950.
27. Halder R K, Uddin M N, Uddin M A, et al. Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. Journal of Big Data, 2024, 11(1): 113.
28. Vedaldi A, Zisserman A. VGG convolutional neural networks practical. Department of Engineering Science, University of Oxford, 2017: 66.
29. Yin L X, Ma C Y, Wang S, et al. Reference values of carotid ultrafast pulse-wave velocity: a prospective, multicenter, population-based study. Journal of the American Society of Echocardiography, 2021, 34(6): 629-641.
30. Gu O, He B, Xiong L, et al. Reconstructive interpolation for pulse wave estimation to improve local PWV measurement of carotid artery. Med Biol Eng Comput, 2024, 62(5): 1459-1473.
31. Mo H, Lang X, Zhang Y, et al. Optimally filtering and matching processing for regional upstrokes to improve ultrasound transit time-based local PWV estimation. Computer Methods and Programs in Biomedicine, 2022, 224: 106997.
32. 肖建, 于龍, 白裔峰. 支持向量回歸中核函數和超參數選擇方法綜述. 西南交通大學學報, 2008, 21(3): 297-303.
33. Salih A M, Raisi E Z, Galazzo I B, et al. A perspective on explainable artificial intelligence methods: SHAP and LIME. Advanced Intelligent Systems, 2025, 7(1): 2400304.
34. Wei C C. Developing an effective arterial stiffness monitoring system using the spring constant method and photoplethysmography. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 151-154.
35. Ahn J M. New aging index using signal features of both photoplethysmograms and acceleration plethysmograms. Healthcare Informatics Research, 2017, 23(1): 53-59.
36. Morris D C. Clinical methods: the history, physical, and laboratory examinations. 3rd edition. Boston: Butterworths,1990: 112.
37. Schober P, Boer C, Schwarte L A. Correlation coefficients: appropriate use and interpretation. Anesth Analg, 2018, 126(5): 1763-1768.

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