Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks

Abstract

In this study, a new approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for detection of electrocardiographic changes in patients with partial epilepsy. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electrocardiographic changes in patients with partial epilepsy. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of training performance and classification accuracies. Receiver operating characteristic (ROC) curves were used to assess the performance of the detection process. The results confirmed that the proposed MLPNN trained with the Levenberg–Marquardt algorithm has potential in detecting the electrocardiographic changes in patients with partial epilepsy.