Primary study on recognition of vascular stiffness based on wavelet scattering neural network_Journal of Biomedical Engineering

Authors：

REN Shuqi , CHEN Zengsheng , DENG Xiaoyan ,  FAN Yubo ,  SUN Anqiang

Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, P. R. China;

Corresponding author：

FAN Yubo, Email: yubofan@buaa.edu.cn; SUN Anqiang, Email: saq@buaa.edu.cn

Keywords：

Korotkoff signal; Vascular stiffness; Cardiovascular diseases; Wavelet scattering; Long short-term memory; Neural network

DOI：

10.7507/1001-5515.202207068

Video：

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Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.

Citation： REN Shuqi, CHEN Zengsheng, DENG Xiaoyan, FAN Yubo, SUN Anqiang. Primary study on recognition of vascular stiffness based on wavelet scattering neural network. Journal of Biomedical Engineering, 2023, 40(2): 244-248. doi: 10.7507/1001-5515.202207068 Copy

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14.	Millasseau S C, Stewart A D, Patel S J, et al. Evaluation of carotid-femoral pulse wave velocity: influence of timing algorithm and heart rate. Hypertension, 2005, 45(2): 222-226.
15.	Tanishiro H, Funakubo A, Fukui Y. An experimental study on the relationship between artificial Korotkoff sounds and self-excited oscillation using a circulatory simulator. Cardiovasc Eng, 2003, 3(3): 85-91.
16.	Babbs C F. The origin of Korotkoff sounds and the accuracy of auscultatory blood pressure measurements. J Am Soc Hypertens, 2015, 9(12): 935-950.e3.
17.	Celler B G, Butlin M, Argha A, et al. Are Korotkoff sounds reliable markers for accurate estimation of systolic and diastolic pressure using brachial cuff sphygmomanometry? IEEE Trans Biomed Eng, 2021, 68(12): 3593-3601.
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21.	Argha A, Celler B G, Lovell N H. A novel automated blood pressure estimation algorithm using sequences of Korotkoff sounds. IEEE J Biomed Health Inform, 2021, 25(4): 1257-1264.
22.	Mallat S. Understanding deep convolutional networks. Philos Trans A Math Phys Eng Sci, 2016, 374(2065): 20150203.
23.	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
24.	Chen Y, Lv J, Sun Y, et al. Heart sound segmentation via Duration Long–Short Term Memory neural network. Appl Soft Comput, 2020, 95: 106540.
25.	Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015: 1412–1421.

1. Khan K N, Khan F A, Abid A, et al. Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning. Physiol Meas, 2021, 42(9). DOI: 10.1088/1361-6579/ac1d59.
2. Khodnapur J P, Das K K. Age-associated changes in vascular health and its relation with erythropoietin. Indian J Physiol Pharmacol, 2021, 65(2): 119-126.
3. Garcia-Rios A, Ordovas J M. Chronodisruption and cardiovascular disease. Clin Investig Arterioscler, 2022, 34(Suppl 1): S32-S37.
4. Laurent S, Boutouyrie P, Asmar R, et al. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension, 2001, 37(5): 1236-1241.
5. Stork S, van den Beld A W, von Schacky C, et al. Carotid artery plaque burden, stiffness, and mortality risk in elderly men: a prospective, population-based cohort study. Circulation, 2004, 110(3): 344-348.
6. Wu S, Jin C, Li S, et al. Aging, arterial stiffness, and blood pressure association in Chinese adults. Hypertension, 2019, 73(4): 893-899.
7. Gomez-Sanchez M, Patino-Alonso M C, Gomez-Sanchez L, et al. Reference values of arterial stiffness parameters and their association with cardiovascular risk factors in the Spanish population. The EVA Study. Rev Esp Cardiol (Engl Ed), 2020, 73(1): 43-52.
8. Mattace-Raso F U S, van der Cammen T J M, Hofman A, et al. Arterial stiffness and risk of coronary heart disease and stroke. Circulation, 2006, 113(5): 657-663.
9. Janic M, Lunder M, Sabovic M. Arterial stiffness and cardiovascular therapy. Biomed Res Int, 2014, 2014: 621437.
10. Tomiyama H, Yamashina A. Non-invasive vascular function tests: their pathophysiological background and clinical application. Circ J, 2010, 74(1): 24-33.
11. Jung J Y, Lee Y B, Kang C K. Novel technique to measure pulse wave velocity in brain vessels using a fast simultaneous multi-slice excitation magnetic resonance sequence. Sensors (Basel), 2021, 21(19): 6352.
12. Weir-McCall J R, Brown L, Summersgill J, et al. Development and validation of a path length calculation for carotid-femoral pulse wave velocity measurement: A TASCFORCE, SUMMIT, and Caerphilly Collaborative Venture. Hypertension, 2018, 71(5): 937-945.
13. Németh Z K, Studinger P, Kiss I, et al. The method of distance measurement and torso length influences the relationship of pulse wave velocity to cardiovascular mortality. Am J Hypertens, 2011, 24(2): 155-161.
14. Millasseau S C, Stewart A D, Patel S J, et al. Evaluation of carotid-femoral pulse wave velocity: influence of timing algorithm and heart rate. Hypertension, 2005, 45(2): 222-226.
15. Tanishiro H, Funakubo A, Fukui Y. An experimental study on the relationship between artificial Korotkoff sounds and self-excited oscillation using a circulatory simulator. Cardiovasc Eng, 2003, 3(3): 85-91.
16. Babbs C F. The origin of Korotkoff sounds and the accuracy of auscultatory blood pressure measurements. J Am Soc Hypertens, 2015, 9(12): 935-950.e3.
17. Celler B G, Butlin M, Argha A, et al. Are Korotkoff sounds reliable markers for accurate estimation of systolic and diastolic pressure using brachial cuff sphygmomanometry? IEEE Trans Biomed Eng, 2021, 68(12): 3593-3601.
18. Anden J, Mallat S. Deep scattering spectrum. IEEE Trans Signal Process, 2014, 62(16): 4114-4128.
19. Bruna J, Mallat S. Invariant scattering convolution networks. IEEE Trans Pattern Anal Mach Intell, 2013, 35(8): 1872-1886.
20. Bruna J, Mallat S. Classification with scattering operators// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs: IEEE, 2011: 1561-1566.
21. Argha A, Celler B G, Lovell N H. A novel automated blood pressure estimation algorithm using sequences of Korotkoff sounds. IEEE J Biomed Health Inform, 2021, 25(4): 1257-1264.
22. Mallat S. Understanding deep convolutional networks. Philos Trans A Math Phys Eng Sci, 2016, 374(2065): 20150203.
23. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
24. Chen Y, Lv J, Sun Y, et al. Heart sound segmentation via Duration Long–Short Term Memory neural network. Appl Soft Comput, 2020, 95: 106540.
25. Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015: 1412–1421.

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