## 2 Related Works  
Many works have been reported in the literature on epileptic seizure detection and classification [11]. Several feature extraction techniques and various classifiers have been developed to achieve high efficiency and accuracy in the characterization of normal and abnormal spikes in epileptic seizure signals. Most of the existing studies on epilepsy detection and classification are focused on the classification of normal, ictal, and interictal EEG epileptic signal rhythms, as reported in [13; 14]. However, only a few works reported the detection and classification of focal and non-focal EEG signals. These works include the proposed method developed in [15], where empirical wavelet transform has been employed for the classification of focal and non-focal EEG signals. [PERSON] and [PERSON] [16] proposed a detection and classification scheme using SVM for the detection of focal and non-focal EEG signals by employing neighborhood component analysis at the feature extraction stage. The resulting accuracy, sensitivity, and specificity of the scheme were 96.1%, 97.6%, and 94.4%, respectively. In [PERSON] and [PERSON] [17], focal and non-focal EEG signals were detected and classified with various classifiers. A differential entropy was used for feature extraction while the accuracy, sensitivity, and specificity of 78.5%, 95.0%, and 95.0%, respectively, were recorded with a random forest classifier as the most robust among the experimented classifiers. Entropy measures were proposed and tested to differentiate between focal and non-focal EEG signals, as reported in [PERSON] et al. [18]. Focal and non-focal EEG signals were decomposed into sub-bands using Flexible Analytic Wavelet Transform (FAWT), while the features were then extracted using a fractal dimension in [PERSON] et al. [19]. Other works reported in the literature regarding the detection and classification of focal and non-focal EEG signals include one reported by [PERSON] et al. [20], who developed a CAD system using complex wavelet transform with an ANFISS classifier. A non-parametric statistical approach was adopted in [21] as an unsupervised method. A Deep learning Convolutional Neural Network (CNN) was applied in [22; 23]. Long short-term memory (LSTM) with a bi-directional learning system was used in the training phase, as reported in [24]. The average accuracy, sensitivity, and specificity reported in the LSTM system were 99.60%, 99.55%, and 99.65%, respectively. Several other approaches were proposed and reported in [25; 26; 27; 28; 29; 30]. However, most of these works focused on either single domain feature extraction techniques, conventional classifiers, or artificial neural network classifiers. Therefore, this work proposed a comprehensive analysis by developing a model that combines several features from different domains. Furthermore, the proposed models include both conventional and recent deep learning structures. Another gap filled by this work is that most of the related works that used the same datasets as this work used a small number of datasets with 50 pairs of focal and non-focal EEG signals, but this work used a complete set of 3750 pairs of focal and non-focal EEG signals.