## 1 Introduction  
Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. It affects as many as 50 million people of all ages worldwide. Uncontrolled seizures can lead to a disruption of the nervous system and physical risks such as injury and even death. It is generally accepted that better seizure control is essential for better brain health in the long term. Brain activity can be recorded by one of several means, such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI). Of these, EEG is the most prevalent because it is simple to effect, low cost, and has high temporal resolution. Its interpretation is commonly used in the study of various brain disorders such as epilepsy, autism, and attention deficit hyperactivity. It is also the most common method in seizure detection and prediction [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. Visual inspection and interpretation of EEG signals are tedious and complex tasks done by experts, which justify research and development of computer-processing methods of EEG that use EEG data representation and machine-learning technics. Investigations of EEG patterns of seizure have shown that, in general, seizure events have been observed within a wide range of EEG bandwidths. Likewise, their amplitude and frequency in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (13-30 Hz), and gamma (30-70 Hz) bands are significantly different than normal [17; 18]. In line with this observation, several studies have used EEG spectral power density (PSD) for seizure detection [3; 7; 8; 19]. PSD is commonly used to evaluate the power of each observed frequency in different neurological brain states. According to previous research, PSDs during seizure events differ significantly from the PSD during rest or sleep states [20], and EEG PSD features have been shown to be able to distinguish between seizure and seizure-free events in the frequency domain [3; 7; 8; 19]. Using the absolute and relative band power of the PSD in 13 frequency sub-bands [21], the authors achieved a 91.36% AUC in a patient-specific study. In another study [22], they found that relative spectral power features led to improved seizure prediction and an average fault rate per hour of 0.1. In a recent study, a comparison of power spectrum analysis and time-frequency analysis was performed. The results revealed that power spectrum features are likely to be seizure markers, and there was a significant difference between the distribution of the power spectrum in seizure segments versus segments in which there were no seizures [23]. Time and frequency domain signal complexity measures have also been used to describe EEG data and serve for the classification of normal and seizure EEG data patterns [2; 5; 7; 11; 12; 13; 17; 18; 19; 24]. Other EEG data descriptions and properties have been used as well--for instance, permutation entropy, which decreases significantly in the transition from a seizure-free phase to a seizure phase [16]; Shannon and logarithmic energy entropy, which decrease during epileptic seizures [4]; spectral entropy, which increases for seizure segments [14]; and wavelet entropy [5; 9]. Although some studies have considered single complexity features in their proposed approaches [5; 16], others have combined several such measures [12; 14]. The clinical utility of automated classification methods that involve neuroimaging techniques have become increasingly important in many areas of health care due to their ability to identify atypical neural activity, such as seizure episodes, without visual inspection. Accordingly, research has addressed the design of end-to-end machine learning (ML) systems to detect seizure segments and determine seizure type. ML offers the opportunity to automatically distinguish between patterns in seizure and seizure-free segments.