Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
DOI:
https://doi.org/10.65455/6pgttp36Keywords:
PPG, Blood Pressure Estimation, Cardiovascular Risk Assessment, Wearable Devices, Signal Processing, MATLABAbstract
This paper systematically studies the non-invasive continuous blood pressure estimation and cardiovascular risk assessment methods based on photoplethysmography (PPG) signals. A complete signal processing flow was constructed using the PhysioNet public dataset, including preprocessing, feature extraction, model construction and validation. The experimental results show that the extraction of heart rate and respiratory rate based on PPG has high accuracy, preliminarily verifying the correlation between the morphological characteristics of PPG and blood pressure parameters. The research further proposed a hierarchical processing architecture to balance the computing efficiency and monitoring depth of wearable devices, providing a reference for the engineering implementation of health monitoring systems.
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Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP
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