Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression

Qingfeng Tang; Shoujiang Xu; Mengjuan Guo; Guangjun Wang; Zhigeng Pan; Benyue Su

doi:10.1007/s13755-022-00172-0

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression

Health Inf Sci Syst. 2022 Apr 21;10(1):7. doi: 10.1007/s13755-022-00172-0. eCollection 2022 Dec.

Authors

Qingfeng Tang^{1

2}, Shoujiang Xu^{2

3}, Mengjuan Guo¹, Guangjun Wang¹, Zhigeng Pan^{2

4}, Benyue Su¹

Affiliations

¹ The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133 China.
² School of Public Health, Hangzhou Normal University, 2318 Yuhangtang Road, Hangzhou, 311121 China.
³ Jiangsu Food and Pharmaceutical Science College, Huai'an, 223023 China.
⁴ School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044 China.

Abstract

Purpose: Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.

Methods: Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.

Results: Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r = - 0.446, P < 0.001 to r = - 0.534, P < 0.001 in men; r = - 0.623, P < 0.001 to r = - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women).

Conclusion: The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

Keywords: Chronological age; Mixed Gaussian model; Support vector regression; Vascular age; Wrist pulse signal.