Investigation of the nonlinearity in the heart rate dynamics

This paper presents the application of a neural network to predict human heart rate. Electrocardiograms were measured from 5 healthy adult human subjects and 5 data sets were constructed calculating instantaneous heart rate from the measured signal. The nonlinear radial basis function neural network was applied to have a one step ahead prediction of the 1000 point heart rate. The results of the prediction are compared to that obtained by a linear autoregressive model. The results show that the neural network performs better 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 statistically similar. This indicates that the heart rate may be controlled nonlinearly by the autonomic nervous system.