# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 1 Introduction  
Epilepsy is a neurological disorder that is capable of causing spontaneous seizures [1]. Based on information given by the World Health Organization (WHO), over 50 million individuals have suffered an epileptic attack [2]. Patients with epilepsy often suffer from loss of consciousness and convulsive movements that can lead to serious physical injury. Early identification of this disease is a crucial health problem. The capability of detecting the occurrence of seizures can improve therapeutic treatment possibility and also the quality of life of epileptic patients. Seizure detection ability allows systems of intercession [3, 4] to be executed for a set of patients for whom surgery or medication have zero impact. Currently, 70% of individuals suffering from epilepsy are able to manage the disease with the right medicine [3]. Pre-surgical evaluations are usually performed to ascertain the location of the disorder. This is done by utilizing a combination of physical exam, history, electroencephalogram (EEG), neuroimaging and other techniques [4, 5]. These studies are insufficient in some patients; hence, focusing on the brain regions susceptible to epilepsy (ital activity) through intracranial EEG is usually recommended. A longer period of hospitalization is regularly needed to detect enough seizures by utilizing intracranial electrodes. If an epileptic seizure is detected, surgical excision is recommended to the patient. In clear-cut lesion scenarios seen on neuro-imaging, surgery provided about 80% of treatment for epileptic patients [6]. A commonly used test for the identification of epilepsy is the use of EEG signals that provide details of the brain's electrical activity [1; 7]. EEG signals are appropriate for describing the state of the brain and can be utilized to examine the brain function. Using the EEG signals, doctors can identify seizure signals and also inter-ictal (between seizure) and ictal (during a seizure) abnormalities in epileptiform. Sometimes, the excess electrical discharge causes the interruption of some brain functions. For this reason, timely and accurate identification of epilepsy at the early stage is necessary for such patients. It allows a reduction in the risk of complication of seizure-related problems. Epileptic seizure detection using EEG signals allows the identification of abnormalities in neurons and evaluates the physiological state of the brain of a human being. Traditionally, the identification of EEG signals is carried out by the visual interpretation of experts (doctors). However, this procedure is time-consuming and can sometimes be subject to human errors. Hence, automation of the identification process of an epileptic seizure using EEG signals becomes an important problem. Here the problem basically involves the extraction of distinguished features of EEG signals for seizure detection [7; 8; 9]. The accuracy of the designed model is determined by the detection of the EEG signal features [1]. Recently, more enhanced techniques indicating high sensitivities of seizure detection with the application of statistical validation have been developed. Statistical evaluation is an ongoing prediction technique requirement inspired by [PERSON] [1]. The classification of EEG signals using a statistical framework and seizure prediction algorithms has been presented in [1]. These approaches have led to the establishment of the generally acknowledged statistical system [10; 11]. The reference [11] presented a random prediction model of seizure and gave comparative results of different models. A series of research studies on seizure identification have been conducted using various signal processing and machine-learning techniques. In [12], the authors decomposed electroencephalogram signal into its frequencies using wavelet transform (WT), and extracted correlation dimension, standard deviation, and largest Lyapunov exponent, which are the three distinctive features from the signals.