Analysis of EEG signals using Lyapunov exponents

Title:

Analysis of EEG signals using Lyapunov exponents

Creator:

Übeyli, Elif Derya

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:dfa4c86e-60ae-4b8c-9128-3d3f27e046c1
uuid:dfa4c86e-60ae-4b8c-9128-3d3f27e046c1

Subject:

Electroencephalogram (EEG) signals, chaotic signal, Lyapunov exponents, multilayer perceptron neural network (MLPNN), training algorithms, and Levenberg-Marquardt algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:3

Harvested from:

CDK

Metadata only:

false