Detecting Epileptic Seizure from Scalp EEG Using Lyapunov Spectrum

Abstract

One of the inherent weaknesses of the EEG signal processing is noises and artifacts. To overcome it, some methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These methods reduced noises, but they were hazardous to patients. In this study, we propose using Lyapunov spectrum to filter noise and detect epilepsy on scalp EEG signals only. We determined that the Lyapunov spectrum can be considered as the most expected method to evaluate chaotic behavior of scalp EEG recordings and to be robust within noises. Obtained results are compared to the independent component analysis (ICA) and largest Lyapunov exponent. The results of detecting epilepsy are compared to diagnosis from medical doctors in case of typical general epilepsy.

Evolution of Dynamical Complexities in Geospace as Captured by Dst Over Four Solar Cycles 1964–2008.

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Significance of using a nonlinear analysis technique, the Lyapunov exponent, on the understanding of the dynamics of the cardiorespiratory system in rats

“…Babloyantz and Destexhe concluded using Lyapunov exponents that a healthy brain is more chaotic than an epileptic brain, and there is variability loss in the brain's electrical activity during an epileptic seizure (43). The diagnostic values of the Lyapunov exponent have also been found to be similar to a diagnosis from medical doctors in the case of typical general epilepsy (27). In another study, the Lyapunov exponent was found to be decreased for epileptic subjects compared with normal subjects (26).…”

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