Key result
A random forest regression model using multi-domain pulse wave features estimated systolic and diastolic blood pressure with mean absolute errors of 1.72 mmHg and 1.40 mmHg, respectively.
Why the study?
Non-invasive blood pressure measurement using pulse wave signals is simple and fast but requires specialized medical knowledge to interpret complex pulse wave features.
Does a random forest regression model using multi-domain pulse wave features accurately estimate non-invasive blood pressure?
Does a random forest regression model using multi-domain pulse wave features accurately estimate non-invasive blood pressure?
Effect estimate: MAE 1.72 mmHg (systolic) and 1.40 mmHg (diastolic)
A novel non-invasive blood pressure estimation system using pulse wave features and random forest regression achieves high accuracy with mean absolute errors below 2 mmHg.
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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Nie et al. (2025) studied Healthy (n=45). Random forest regression using multi-domain pulse wave features was evaluated on Mean absolute error of systolic and diastolic blood pressure estimation (MAE 1.72 mmHg (systolic) and 1.40 mmHg (diastolic)). A random forest regression model using multi-domain pulse wave features estimated systolic and diastolic blood pressure with mean absolute errors of 1.72 mmHg and 1.40 mmHg, respectively.
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