Non-invasive Real-time Blood Pressure Prediction Method Based on Machine Learning

摘要

Aiming at the accuracy of continuous monitoring of blood pressure by photoelectric method, and being unable to establish a unified prediction model for all individuals in the population due to the differences of human characteristics, this paper proposes a blood pressure prediction method based on principal component analysis (PCA) and genetic algorithm (GA) to optimize machine learning model. The method firstly processes the photoplethysmography (PPG) signal, the electrocardiography (ECG) signal and the human body features to form a feature matrix, and uses a machine learning model to perform regression training on the feature matrix and the real-time blood pressure value measured by the mercury sphygmomanometer. Then, the GA is used to optimize the parameters to establish an optimal blood pressure prediction model. The experimental results show that compared with the traditional SVR, the proposed method could improve the predictive accuracy by 10%-15%.