Non-Invasive Blood Pressure Estimation Using Multi-Domain Pulse Wave Features and Random Forest Regression
Abstract
With more attention paid to the prevention of cardiovascular diseases, convenient and non-invasive methods of blood pressure measurement are gradually receiving attention. Non-invasive blood pressure measurement based on pulse wave signals is simple and fast but requires specialized medical knowledge to deal with complex pulse wave features. The aim of this study was to map pulse signal features to systolic/diastolic blood pressure values using machine learning methods. In this study, a flexible piezoelectric sensor and its circuit were designed to measure and preprocess pulse signals. Then, 32 features of pulse signals were extracted from the time domain, frequency domain and wavelet domain, and a random forest regression model was introduced to estimate diastolic/systolic blood pressure. Finally, model optimization and effect evaluation were carried out. The mean absolute errors of systolic and diastolic blood pressures estimated by the proposed system are within 1.72 mmHg and 1.40 mmHg, which meets the requirement of the Association for the Advancement of Medical Instrumentation with a mean absolute error below 5 mmHg. The system is expected to enable simple daily blood pressure monitoring applications.
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