Comparison Between Non-Linear Autoregressive and Non-Linear Autoregressive with Exogeneous Inputs Models for Predicting Cardiac Ischemic Beats

The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are validated by using ECG signals acquired from MIT-BIH database. The results have depicted that the NARX architecture with 2 neurons in hidden layer and 1 delay line outperformed with least Mean Square Error (MSE) of 0.0001 for detecting the ischemic beats in ECG.