Machine Learning-Based Continuous Blood Pressure Estimation Using Noisy Photoplethysmography Signals
Keywords:
continuous blood pressure estimation; photoplethysmography; machine learning; noise robustness; clinical deployment; data governance; fairnessAbstract
Continuous blood pressure monitoring is central to cardiovascular risk management, yet conventional cuff-based devices remain episodic and intrusive. Photoplethysmography provides a low-cost optical signal that can be embedded in wearable devices, but ambulatory recordings are degraded by motion, sensor displacement, ambient light, and tissue heterogeneity. Machine learning has been proposed to map noisy photoplethysmographic waveforms to continuous blood pressure estimates. However, reliable translation requires a systems perspective that extends beyond model architecture. This article examines the structural trade-offs involved in signal conditioning, feature extraction, learning strategies, data governance, deployment, and regulatory alignment. It argues that robust systems must combine adaptive preprocessing with uncertainty-aware models, representative datasets, validation against clinical standards, and explicit fairness considerations. A layered design is discussed in which local signal restoration, population-level calibration, and institutional governance operate together. The analysis integrates evidence from physiological sensing, machine learning, and health policy to identify barriers and pathways toward sustainable continuous blood pressure estimation. Emphasis is placed on cross-domain lessons such as pipeline governance, auditability, and lifecycle maintenance. The conclusion highlights the need for coordinated technical and regulatory evolution rather than isolated algorithmic optimization.
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