Regression tree model for blood pressure estimation using the minimalist characteristics of photoplethysmography signal
Objective To propose a regression tree model for the estimation of blood pressure using the minimalist characteristics of photoplethysmography(PPG)signals.Methods Fifteen characteristic parameters were extracted from the PPG signals,and the 4 parameters with the highest correlations with blood pressure were screened using the Spearman correlation coefficient to construct a regression tree model for blood pressure estimation using the minimalist characteristics.Results The estimation errors of systolic and diastolic blood pressures in the constructed model were(-0.02±3.63)mmHg and(-0.04±2.10)mmHg,respectively.Conclusion The proposed regression tree model has a simple structure and high accuracy,which is of great significance for using a single-channel PPG signal for blood pressure estimation in wearable devices.
photoplethysmographyminimalist characteristicsSpearman correlation coefficientblood pressure estimation model
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