# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 1 Introduction
Different techniques are applied for the classification of epileptic seizures and the wavelet-chaos-neural network approach has depicted good performance. In [13], fast Fourier transform (FFT) was utilized for feature extraction and decision tree classifier was utilized to classify these features. The paper [14] applied two distinctive approaches: principal component analysis (PCA) and genetic algorithm (GA) on several linear and non-linear algorithms. Application of principal component analysis on a non-linear algorithm produced better outcomes compared to a genetic algorithm. Reference [15] depicted a review of wavelet-based approaches to detect epileptic seizures. Reference [16] proposed an approach that applies training and testing sets to classify and detect electroencephalogram non-seizure and seizure signals via the extraction of higher-order spectral features. The support vector machine (SVM) was applied for classification purposes [16]. Reference [17] used empirical mode decomposition to analyze EEG signals. The Hilbert-Huang transform (HHT) was used for the extraction of intrinsic mode functions. These functions were utilized as features to differentiate EEG signals. Reference [18] compared brain activities using distinctive brain regions and analyzed groups of ECG time series. In [19], using the non-linear dynamics of EEG signals quantified by correlation coefficients and largest Lyapunov exponent and wavelet-based methodology, the analysis of EEG signals was performed. Reference [20] presented logistic model trees that use statistical features based on an optimal allocation technique for detecting seizure from EEG signals. Some statistical features were extracted from EEG signals and these features were utilized as input in a logistic model tree (LMT) for epileptic seizure identification. The presented method was tested using benchmark EEG dataset. The papers mentioned above depict different methodologies used for extraction of features and classification purposes. The accuracies of these designed models are important performance characteristics of the designed systems. In the paper [21] that used discrete wavelet transform (DWT), the EEG signals were fragmented into frequency sub-bands leading to the extraction of statistical features. The PCA, independent components analysis (ICA) and linear discriminant analysis (LDA) were utilized for data size reduction. Using the obtained features, SVM was applied to classify epileptic seizures from non-epileptic seizures. In [22], data points of University were created via the selection of Universumfrom a dataset of EEG itself otherwise known as inter-ictal EEG signals. Using feature extraction techniques and Universum SVM, the authors in [22] performed seizure classification. In [23], a group of important features was extracted from the EEG data, utilizing NCA (neighbourhood component analysis). The system performance was assessed utilizing SVM, AdaBoost (adaptive boosting), K-NN and random forest classifiers. References [24; 25] used convolutional neural networks (CNNs) to analyze the EEG signals. The CNN model was utilized to extract features and the extracted features were used to perform the classification of normal, preictal and seizure classes. The papers [26; 27] used a machine learning approach for the detection of real-time seizure from intracranial EEG signal. Here, the authors extracted spectral and temporal features and utilized them for the training of the pattern recognition component. Reference [28] used linear discriminant analysis and triadic wavelet decomposition-based features, and K-NN classifiers to classify the signals of the seizure. The major objective of this research is to detect seizures caused by epilepsy. The seizure detection ability allows systems of intercession to be executed for a set of patients where surgery or medication have had zero impact. The structures of the above-constructed systems are complicated and basically, they include the extraction of features and classification stages.