## 1 Introduction
In [14], one-dimensional (1D) LBP-based features are extracted for the classification of epileptic seizure EEG signals and obtained classification accuracy of 95.67% when classifying seizure, seizure-free and the normal classes of EEG signals. In [15], the authors have developed a method based on the LBP of the Gabor filter-decomposed EEG signals followed by the nearest neighbor classifier and classified seizure-free and seizure EEG signals with a classification accuracy of 98.33%. In a recent study [16], key-point-based LBP has been used for the classification of seizure EEG signals. The authors obtained classification accuracy of 98.80% with a standard deviation (SD) of 0.11% in classifying seizure, seizure-free and normal groups of EEG signals. In [17], the authors modeled EEG signals by the linear prediction (LP) process, and the energy of the modeling error was used for the epileptic seizure EEG signal classification task. They achieved classification accuracy of 94% in classifying seizure and seizure-free EEG signals. Epileptic seizure EEG signal classification has been performed using the fractional LP model in [18]. They computed signal energy and fractional LP error energy as features for the classification of seizure-free and seizure EEG signals with the SVM classifier and obtained 95.33% classification accuracy in classifying seizure and seizure-free EEG signals. The PCA has been used with the cosine radial basis function (RBF) neural network for the classification of epileptic EEG signalsin [19], and the authors yielded classification accuracy of 96.6% in classifying seizure, seizure-free and normal EEG signals. The short-term maximum Lyapunov exponent (STLmax) has been estimated from the EEG recordings of intractable epilepsy patients in [20]. They estimated low STLmax in the focal area of the brain during the pre-ictal or inter-ictal state. In [21], the authors processed one hour-long pre-ictal segments and estimated the STLmax profiles from them. They modeled spatial STLmax maps with the combination of two Gaussian functions to extract parameters of the fitted models, which in turn helped to extract quantitative information about the spatial distribution of the STLmax. For three out of four patients, the authors found low STLmax levels in the brain regions of seizure origination. In [22], the authors have used permutation entropy (PE)-based [23] spatio-temporal analysis for EEG synchronization activity in 24 patients suffering from absence seizures and 40 healthy subjects. They found a higher PE value for fronto-temporal areas, and it showed a lower value when computed from the parietal/occipital area. In [24], the authors proposed the permutation Renyi entropy (PEr) for the classification of seizure and seizure-free EEG signals of patients diagnosed with childhood absence epilepsy. Their proposed algorithm performed better than conventional PE. In this paper, we have presented a new method for the classification of seizure, seizure-free and normal classes of EEG signals. The proposed method computes the complexity of the analyzed signals in multi-level oscillatory scales. Entropy features have been used extensively as a measure of the complexity in biomedical signal processing [25; 26; 27; 28]. Traditional measures of entropies, namely Shannon entropy [29], Kolmogorov-Sinai (K-S) entropy [30], approximate entropy (ApEn) [31] and sample entropy (SEn) [32], quantify the complexity of time series. These methods measure the complexity of the time series in a single scale, which may not be able to quantify the underlying dynamics of real time series [33]. The signals generated from complex biological systems consist of more than one spatiotemporal scales [33]. In [33], the authors have proposed multi-scale entropy (MSEn), which computed SEn over different scales of a signal as a measure of complexity. The multi-scale version of SEn has been utilized for the complexity measure of multivariate EEG signals in [34].