Investigation of the nonlinearity in the heart rate dynamics

This paper presents the application of a neural network to predict human he art rate. Electrocardiograms were measured from 5 healthy adult human subje cts and 5 data sets were constructed calculating instantaneous heart rate f rom the measured signal. The nonlinear radial basis function neural network was applied to have a one step ahead prediction of the 1000 point heart ra te. The results of the prediction are compared to that obtained by a linear autoregressive model. The results show that the neural network performs be tter than the autoregressive model in predicting heart rate for 2 data sets while for the other 3 data sets the performance of the two models is stati stically similar. This indicates that the heart rate may be controlled nonl inearly by the autonomic nervous system. (C) 2001 IPEM. Published by Elsevi er Science Ltd. All rights reserved.