Summary
In this study, a new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. 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 electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The computed Lyapunov exponents of the EEG 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 potentiality in detecting the electroencephalographic changes.
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Extract
Analysis of Eeg Signals Using Lyapunov Exponents
1. Introduction
The electroencephalogram (EEG) is a complex and aperiodic time series which is a sum over a very large number of neuronal membrane potentials. Despite rapid advances of neuroimaging techniques, EEG recordings continue to play an important role in both, diagnosis of neurological diseases and understanding psychophysiological processes. In order to extract relevant information from recordings of brain electrical activity, a variety of computerized analysis methods has been developed. Most methods are based on the assumption that the EEG is generated by a highly complex linear system, resulting in characteristic signal features like nonstationary or unpredictability [1]. Much research with nonlinear methods revealed that the EEG is generated by a chaotic neural process of low dimension [2-4]. According to these reports, the EEG has a finite noninteger correlation dimension and a positive Lyapunov exponent. Furthermore, the distinct states of brain activity had different chaotic dynamics quantified by nonlinear invariant measures such as correlation dimensions and Lyapunov exponents [2-4].The EEG signals reflect the electrical activity of the brain. The study of the brain electrical activity, through the electroencephalographic r...See the full content of this document
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