Study on a quantitative analysis method for pulse signal by modelling its waveform in time and space domain.
10.7507/1001-5515.201904024
- Author:
Yongxin CHOU
1
,
2
;
Aihua ZHANG
3
;
Jicheng LIU
4
;
Jiajun LIN
5
;
Xufeng HUANG
2
Author Information
1. School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, Jiangsu 215500, P.R.China
2. The East China Science and Technology Research Institute of Changshu Co., Ltd, Suzhou, Jiangsu 215500, P.R.China.
3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, P.R.China.
4. School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, Jiangsu 215500, P.R.China.
5. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, P.R.China.
- Publication Type:Journal Article
- Keywords:
analytical modeling in the time-space domain;
pulse signal;
pulse to pulse interval;
pulse waveform;
quantitative analysis
- From:
Journal of Biomedical Engineering
2020;37(1):61-70
- CountryChina
- Language:Chinese
-
Abstract:
In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given. The spatial-temporal analytical modeling method was applied to the pulse waves of healthy subjects from the international standard physiological signal sub-database Fantasia of the PhysioNet in open-source, and we derived some changes in heartbeat rhythm and hemodynamic generated by aging and gender difference from the analytical models. The model parameters were employed as the input of some machine learning methods, e.g. random forest and probabilistic neural network, to classify the pulse waves by age and gender, and the results showed that random forest has the best classification performance with Kappa coefficients over 98%. Therefore, the space-time analytical modeling method proposed in this study can effectively quantify and analyze the pulse signal, which provides a theoretical basis and technical framework for some related applications based on pulse signals.
