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

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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.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the agreement of the funding but are available from the corresponding author on reasonable request.

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Acknowledgements

We are grateful to Hangzhou Health Management Center.

Funding

This work is supported by National Nature Science Foundation of China (No. 61603003), Anhui Provincial Natural Science Foundation (No. 2108085QF269), Zhejiang Provincial Natural Science Foundation (No. LZ21F020008), and the Key Project on Anhui Provincial Natural Science Study by Colleges and Universities (No. KJ2019A055).

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Authors and Affiliations

  1. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133, China

    Qingfeng Tang, Mengjuan Guo, Guangjun Wang & Benyue Su

  2. School of Public Health, Hangzhou Normal University, 2318 Yuhangtang Road, Hangzhou, 311121, China

    Qingfeng Tang, Shoujiang Xu & Zhigeng Pan

  3. Jiangsu Food and Pharmaceutical Science College, Huai’an, 223023, China

    Shoujiang Xu

  4. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhigeng Pan

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  1. Qingfeng Tang
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Contributions

All research done by the authors. Su and Pan designed this research, manuscript writing was mainly accomplished by Tang. Data collection was mainly accomplished by Xu. Wang and Guo contributed in data preprocessing.

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Correspondence to Zhigeng Pan or Benyue Su.

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All subjects agreed to participate and undertake the tests given by the doctor. This study complied with the principles of the Declaration of 1964 Helsinki and was approved by the Ethics Committee of our institution.

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Tang, Q., Xu, S., Guo, M. et al. Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression. Health Inf Sci Syst 10, 7 (2022). https://doi.org/10.1007/s13755-022-00172-0

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