Evaluation of Prediction Capability of Non-recursion Type 2nd-order Volterra Neuron Network for Electrocardiogram

Abstract

The prediction accuracy of QRS wave that show electric excitement by the ventricle of the heart is low in linear predictions of electrocardiogram (ECG) used a conventional linear autoregressive model, and it is a problem that the prediction accuracy is not improved even if the prediction order is set second and third or more. The causes are that QRS wave generated by the nonlinear generation mechanism and the nonlinear components which the linear models cannot predict is included in ECG. Then, Non-recursion type 1^st-order Volterra neuron network (N1VNN) and Non-recursion type 2^nd-order Volterra neuron network (N2VNN) were evaluated about nonlinear prediction accuracies for ECG. The results of comparing nonlinear predictions of both networks showed that N2VNN is 17.6 % smaller about the minimum root mean square error indicating prediction accuracy than N1VNN.