Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression
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 are non-linear dynamics: mathematical tools applied to biomedical problems with the purpose of extracting features or quantifying EEG data. In this work we studied epileptic seizure detection in all brain rhythms using independent component analysis (ICA) representation of multivariate EEG signals coupled with the largest Lyapunov exponent (LLE). With this information a logistic regression classification is proposed with the aim of discriminating between seizure and non-seizure. Preliminary experiments with 36 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.