Classification of PCG Signals Using A Nonlinear Autoregressive Network with Exogenous Inputs (NARX)

Artificial neural networks have proven high efficiency in the classification of features in many application fields. It is widely proved that dynamic recurrent networks perform better than any other types of networks, for the feedback connections and the embedded memory. In this paper, we use a dynamic recurrent network called the nonlinear autoregressive network with exogenous inputs (NARX) for the normal/abnormal classification of phonocardiography (PCG) signals. Up to the authors' knowledge, this is the first time that the NARX network is used for such classification task. The network is trained and tested using a dataset of 6316 feature vectors, each vector of length 27 features. The evaluation of this network is achieved at different sizes of the embedded memory. The experimental results show that the NARX network outperforms other two networks, and its performance increases as a function of the memory size till a certain value. Further increase of the memory size degrades the performance as a results of model overfitting.