[Study on a quantitative analysis method for pulse signal by modelling its waveform in time and space domain]
Yongxin Chou1, Aihua Zhang2, Jicheng Liu3
1School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, Jiangsu 215500, P.R.China;The East China Science and Technology Research Institute of Changshu Co., Ltd, Suzhou, Jiangsu 215500, P.R.China.
Summary
A novel time-space analytical model quantifies pulse signals, revealing age and gender differences in heartbeat rhythm and hemodynamics. Machine learning models, particularly random forest, achieved over 98% accuracy in classifying pulse waves.
Area of Science:
- Biomedical Engineering
- Physiological Signal Analysis
- Computational Medicine
Background:
- Pulse signals are crucial for assessing cardiovascular health.
- Quantitative analysis of pulse wave morphology and period is essential for accurate diagnosis.
- Existing methods may lack comprehensive spatio-temporal analysis.
Purpose of the Study:
- To propose a time-space analytical modeling and quantitative analysis method for pulse signals.
- To develop a mathematical model integrating pulse signal period and baseline.
- To investigate age and gender-related variations in pulse wave characteristics.
Main Methods:
- Developed a pulse space-time analytical model with 12 parameters based on pulse signal generation.
- Presented a parameter estimation process including optimization, constraints, and boundary conditions.
- Applied the model to healthy subject pulse waves from the PhysioNet Fantasia database.
- Utilized machine learning (random forest, probabilistic neural network) for pulse wave classification.
Main Results:
- Derived changes in heartbeat rhythm and hemodynamics related to aging and gender differences.
- Random forest achieved the highest classification performance for pulse waves by age and gender, with Kappa coefficients exceeding 98%.
- The model effectively quantifies and analyzes pulse signal morphology and period.
Conclusions:
- The proposed space-time analytical modeling method provides effective quantification and analysis of pulse signals.
- This method offers a theoretical basis and technical framework for pulse signal-based applications.
- The findings highlight the potential for advanced physiological signal analysis in understanding human health variations.
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