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Reconstruction of central aortic pressure based on TCN-attention model
Wenyan Liu1, Yajie Cao1, Yali Fu1
1School of Information and Communication Engineering, North University of China, Taiyuan, China.
Frontiers in Physiology
|November 3, 2025
Summary
Accurate central aortic pressure measurement aids cardiovascular disease prevention. A new TCN-Attention model reconstructs this vital hemodynamic biomarker more effectively than traditional methods.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Machine learning in healthcare
Background:
- Abnormal blood pressure is a significant cardiovascular disease risk factor.
- Central aortic blood pressure is a superior hemodynamic biomarker compared to peripheral measurements.
- Current invasive and non-invasive methods for central aortic pressure measurement have limitations.
Purpose of the Study:
- To develop an accurate non-invasive method for reconstructing central aortic pressure.
- To improve cardiovascular risk assessment and patient management.
Main Methods:
- Utilized a TCN-Attention model for time series data analysis.
- The model extracts both local patterns (mutations, key time points) and global patterns (trends, periodicity).
- Attention mechanism compensates for TCN's limitations in global feature extraction.
Main Results:
- The proposed TCN-Attention model demonstrated higher accuracy in reconstructing central aortic pressure compared to the standard TCN model.
- Experimental results validate the model's effectiveness.
Conclusions:
- Precise central aortic pressure measurement is clinically valuable for cardiovascular disease prevention, diagnosis, and treatment.
- The TCN-Attention model offers a promising non-invasive approach for accurate central aortic pressure reconstruction.
