A multi-feature fusion method to estimate blood pressure by ppg

Abstract

Effective features extracted from Photoplethysmogram (PPG) are the central for estimating accurately blood pressure (BP). To make extracted features have a strong correlation with real blood pressure, a model based on feature fusion is presented to evaluate blood pressure. To divide pulse wave into two types of dicrotic wave and non-dicrotic wave, different types of waveforms use different extracted features, and wavelet transform is used to remove the noise from the extracted features. Linear regression model and neural network are evaluation models, and Matlab system identification toolboxes are used to recognize the model parameters. The experiment results have shown that extracted features have a correlation with systolic pressure (SP) and diastolic pressure (DP). The value of blood pressure can be calculated based on features extracted from PPG. What’s more, the accuracy of the fusion feature model is improved compared with the traditional method only by using one type of extracted feature method for all the pulse waveforms.