Sun, Hao;
Ma, Junling;
Li, Bao;
Liu, Youjun;
Liu, Jincheng;
Wang, Xue;
Baier, Gerold;
... Zhang, Liyuan; + view all
(2025)
Estimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method.
International Journal for Numerical Methods in Biomedical Engineering
, 41
(1)
, Article e3905. 10.1002/cnm.3905.
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Baier_CentralAorticPressureWaveforms Zhang2025.pdf - Accepted Version Access restricted to UCL open access staff until 8 January 2026. Download (2MB) |
Abstract
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators. We collected data from 260 patients who underwent catheterization surgery, using measured CAPW and personalized physiological indicators (e.g., weight, body mass index (BMI), radial mean arterial pressure (MAP), heart rate (HR), cardiac output (CO), radial systolic blood pressure (SBP), and radial diastolic blood pressure (DBP)) as input for neural network training. The output of the neural network are the Gaussian characteristic parameters of the single-period decomposed CAPW. The neural network model was constructed using the model-agnostic meta-learning (MAML) algorithm framework. Applying the physical characteristics of CAPW to the loss function, served to increase the constraints on the output and improve the accuracy of CAPW estimation. To verify the accuracy of the model, we compared measured and estimated CAPW in 52 patients. The results are consistent with a normalized root mean square error (NRMSE) of 0.0206. The predictions had low biases, namely SBP: 4.97 ± 4.42 mmHg, DBP: 4.78 ± 5.98 mmHg, and MAP: 0.35 ± 3.36 mmHg. The results demonstrate the accuracy and practicability of the approach to estimate CAPW. It can provide personalized parameters to calculate myocardial ischemia indicators (e.g., instantaneous wave-free ratio [iFR] and fractional flow reserve [FFR]) and may contribute to the early monitoring and prevention of cardiovascular diseases.
| Type: | Article |
|---|---|
| Title: | Estimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method |
| Location: | England |
| DOI: | 10.1002/cnm.3905 |
| Publisher version: | https://doi.org/10.1002/cnm.3905 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Central aortic pressure wave; meta‐learning; neural network; physics‐driven |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Cell and Developmental Biology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203870 |
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