From Pulse to Insight: An End-To-End Explainable Deep Spatio-Temporal Fusion Network for Cuffless Blood Pressure
Insights
This study presents a novel cuffless blood pressure monitoring system using only photoplethysmography (PPG) signals. The deep learning model achieves accurate, non-invasive, and interpretable estimations, improving cardiovascular care.
Area of Science:
- Biomedical Engineering
- Cardiovascular Technology
- Artificial Intelligence in Healthcare
Background:
- Continuous blood pressure monitoring is crucial for cardiovascular disease management.
- Traditional cuff-based methods are invasive and inconvenient for long-term use.
- Photoplethysmography (PPG) signals offer a potential non-invasive alternative.
Purpose of the Study:
- To develop and validate a novel cuffless blood pressure estimation system using only PPG signals.
- To integrate deep learning for robust feature extraction and accurate blood pressure prediction.
- To enhance clinical decision-making through an interpretable AI model.
Main Methods:
- Utilized a deep spatio-temporal fusion network with convolutional and recurrent layers.
- Trained and validated the model on the PulseDB dataset.
- Employed Grad-Cam for model explainability and developed a custom PPG acquisition device.
Main Results:
- Achieved mean absolute errors of 3.57 mmHg (systolic) and 2.20 mmHg (diastolic).
- Demonstrated low bias and acceptable variability, meeting AAMI and BHS validation standards.
- The system provides reliable, non-invasive, and interpretable cuffless blood pressure monitoring.
Conclusions:
- The proposed system offers a reliable, non-invasive, and interpretable solution for continuous blood pressure monitoring.
- This technology has significant potential to improve early detection and management of hypertension and cardiovascular risks.
- Explainability of the AI model enhances trust and clinical applicability.
Abstract:
Continuous blood pressure monitoring is essential for timely detection and proper management of cardiovascular diseases. Though traditional cuff-based methods are accurate and widely used, they are inconvenient for repetitive and long-term monitoring. In this study, we introduce a novel end-to-end cuffless blood pressure estimation system that only uses photoplethysmography (PPG) signal. Our approach employs a deep spatio-temporal fusion network, integrating convolutional and recurrent layers to extract robust features from the PPG waveform. The model is trained and validated on the PulseDB dataset. Furthermore, we used Grad-Cam for explainability which helps in the decision-making process. We also developed a custom PPG signal acquisition device to facilitate real-time blood pressure monitoring via a dedicated application. The proposed system achieved mean absolute errors of 3.57 mmHg for systolic and 2.20 mmHg for diastolic blood pressure, along with low bias and acceptable variability which also meets both AAMI and BHS validation standards. This work demonstrates a reliable, non-invasive, and interpretable solution for cuffless continuous blood pressure monitoring, offering significant potential to improve cardiovascular care in clinical settings.Clinical relevance- This study provides a cuffless, real-time method for monitoring systolic and diastolic blood pressures using only PPG signal. The model's explainability enhances trust, making it a reliable tool for early detection and management of hypertension and related cardiovascular risks.
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