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Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression

Abstract : Seizure detection plays a central role in most aspects of epilepsy care. Understanding the complex epileptic signals system is a typical problem in electroencephalographic (EEG) signal processing. This problem requires different analysis to reveal the underlying behavior of EEG signals. An example of this is the non-linear dynamic: mathematical tools applied to biomedical problems with the purpose of extracting features or quantifying EEG data. In this work, we studied epileptic seizure detection independently in each brain rhythms from a multilevel 1D wavelet decomposition followed by the independent component analysis (ICA) representation of multivariate EEG signals. Next, the largest Lyapunov exponents (LLE) and their scaling given by its ± standard deviation are estimated in order to obtain the vectors to be used during the training and classification stage. With this information, a logistic regression classification is proposed with the aim of discriminating between seizure and non-seizure. Preliminary experiments with 99 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity.
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  • HAL Id : hal-02868899, version 1
  • OATAO : 26116

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Antonio Quintero Rincón, Máximo Flugelman, Jorge Prendes, Carlos d'Giano. Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression. Revista Argentina de Bioingeniería, 2019, 23 (2), pp.17-24. ⟨hal-02868899⟩

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