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Non-Invasive BP Estimation via Carotid-Brachial-Radial Coupling: A Physics-Informed Asymmetric Cycle Network Approach
IEEE Transactions on Bio-Medical Engineering
|June 22, 2026
Summary
A new physics-informed deep learning model, PIAC-Net, improves cuffless blood pressure monitoring by integrating central carotid artery data with peripheral photoplethysmogram signals. This approach enhances accuracy and interpretability for hypertension management.
Area of Science:
- Biomedical Engineering
- Cardiovascular Physiology
- Artificial Intelligence in Medicine
Background:
- Cuffless blood pressure (BP) monitoring using photoplethysmogram (PPG) shows promise for hypertension management but suffers from accuracy and interpretability issues.
- Limitations stem from PPG's inability to capture central hemodynamic drivers and inherent physical constraints.
Purpose of the Study:
- To develop a novel multimodal, physics-informed deep learning framework (PIAC-Net) for accurate and interpretable cuffless BP monitoring.
- To integrate central arterial information, specifically carotid blood flow velocity, with peripheral PPG signals.
Main Methods:
- A physics-informed deep learning framework (PIAC-Net) was developed, integrating multi-site physiological data from carotid, brachial, and radial arteries.
- Carotid blood flow velocity was incorporated as a central hemodynamic signal, supplementing PPG data.
- Hybrid physical loss functions, combining 1D Navier-Stokes equations and 0D Windkessel boundary conditions, enforced hemodynamic conservation on the brachial waveform.
Main Results:
- PIAC-Net demonstrated superior accuracy compared to existing methods in both synthetic (N=4374) and in-house (N=1765) datasets.
- Mean absolute errors for systolic/diastolic BP were 1.82/1.51 mmHg (synthetic) and 7.08/6.38 mmHg (in-house).
- Integrating carotid data reduced estimation errors by over 50%, highlighting its role as a primary accuracy factor. Physics-based constraints ensured performance even with limited data.
Conclusions:
- The dual-fusion paradigm of "central driver-peripheral response" and "data-physics" integration offers a clinically viable solution for high-precision BP monitoring.
- PIAC-Net provides a significant advancement in cuffless BP monitoring, addressing key limitations of PPG-based methods.
- This approach holds potential for revolutionizing hypertension management through improved accuracy and interpretability.
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Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
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01:17Measurement of Blood Pressure
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Monitoring Both Arms:
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Monitoring Both Arms:
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