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.
Haque
M A
MA
Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. ahaque@eebuet.bdmail.net
Hasan
M K
MK
Tazawa
H
H
D000328:Adult; D001341:Autonomic Nervous System / Q000502:physiology; D001698:Biomedical Engineering; D004562:Electrocardiography / Q000706:statistics & numerical data; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics