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Dynamic beat-to-beat blood pressure estimation using a multi-modal wearable deep learning approach
Qiao Li1, Zichao Shen2, Mohamed Almadi3,4
1Department of Biomedical Informatics, Emory University, Atlanta, GA 30322 United States of America.
Physiological Measurement
|July 2, 2026
Summary
This study introduces a novel deep learning method using multi-modal sensor fusion for accurate, calibration-resilient cuffless blood pressure (BP) monitoring. The approach significantly improves BP estimation accuracy during dynamic changes.
Area of Science:
- Biomedical Engineering
- Physiological Monitoring
- Machine Learning in Healthcare
Background:
- Cuffless blood pressure (BP) monitoring faces calibration drift due to reliance on indirect surrogates.
- Existing methods like pulse transit time (PTT) and photoplethysmography (PPG) lack direct pressure measurement.
- Superficial temporal artery tonometry (STAT) offers high-fidelity pressure waveform data but requires integration.
Purpose of the Study:
- To develop a multi-modal deep learning framework for calibration-resilient cuffless blood pressure monitoring.
- To integrate superficial temporal artery tonometry (STAT) with electrocardiography (ECG) and photoplethysmography (PPG) signals.
- To overcome the physiological limitations of current BP monitoring technologies.
Main Methods:
- A custom wearable device simultaneously acquired ECG, PPG, and STAT signals during dynamic BP perturbations.
- Features extracted included heart rate (HR), PTT, and BP-related metrics from STAT.
- A temporal convolutional network (TCN) model was employed to analyze complex, non-linear dependencies between multi-modal features and beat-to-beat BP.
Main Results:
- The TCN model achieved a Mean Absolute Difference (MAD) of 5.58 mmHg for systolic BP, 4.39 mmHg for mean BP, and 4.34 mmHg for diastolic BP.
- Leave-One-Subject-Out Cross-Validation on 29 recordings demonstrated superior performance over PTT and STAT-only baselines.
- The TCN model showed significantly lower errors during dynamic BP fluctuations compared to baseline models (p < 0.05).
Conclusions:
- Fusing tonometry-derived pressure morphology with hemodynamic timing features effectively addresses limitations of conventional PTT methods.
- The proposed multi-modal deep learning framework offers a robust solution for continuous, calibration-resilient BP estimation.
- This technology holds promise for improved non-invasive and accurate blood pressure monitoring.
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