A hybrid model for blood pressure prediction from a PPG signal based on MIV and GA-BP neural network

Abstract

Continuous monitoring of blood pressure for a long time, which is necessary for heart disease patients, is useful for the doctor to adjust the ideal treatment. In this paper, a hybrid model for blood pressure estimation from a photoplethysmography (PPG) signal based on Mean Impact Value (MIV) and Genetic Algorithm-Back Propagation (GA-BP) Neural Network is formulated. More than 4500 heartbeats training data were extracted from the University of Queensland Vital Signs Dataset. The MIV method is used to evaluate the input variable of BP neural network and simplify the neural network model. 13 parameters were selected as the input variable for BP neural network from 21 parameters which were extracted from PPG signal. In addition, In order to overcome the problem that BP neural network is easy to fall into the local minimum, we use GA algorithm to optimize the initial weights and thresholds of BP neural networks and then establish the GA-BP model to predict blood pressure. Compared with the other BP neural network structures, Simulation results show that the algorithm proposed in this paper can predict blood pressure with higher accuracy.