# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
The main symptoms of epilepsy include staring, confusion, uncontrollable shaking, loss of consciousness, and psychological symptoms such as fear [8]. Epileptic seizures are classified based on the part of the brain where abnormal brain activity begins. According to the International League Against Epilepsy (ILAE), a diagnosis of epilepsy requires at least one seizure without a known trigger, or a risk of recurrent seizures or an epilepsy syndrome. Pediatric epilepsy can be particularly complex due to the diverse expressions of syndromes that require diagnosis, evaluation, and specific treatment. Children with epilepsy may differ from adults with the condition, particularly in the expression of age-specific epileptic syndromes. This requires careful consideration of factors such as diagnosis, classification of epilepsy, evaluation to determine the pathology, and decision-making for treatment. A thorough understanding of these factors can help researchers make an accurate diagnosis of epilepsy and guide appropriate testing and treatment decisions [9]. Epilepsy is a serious disorder characterized by recurrent seizures due to sudden dysfunction in the brain. These seizures can disrupt normal brain function and may cause other side effects such as amnesia, depression, and other mental disorders. Early identification of epilepsy is important in order to take appropriate action to prevent potential consequences and ensure the patient's health. In recent decades, there has been active research on the automatic detection of seizures and the use of EEG signals for the detection of epilepsy [9; 10]. As it mentioned before, EEG is a non-invasive method which is commonly used in the clinical diagnosis of epilepsy. However, it can be tedious and time-consuming for neurologists to manually identify seizures from EEG recordings. As a result, there is a need for the development of a reliable automatic epilepsy detection system that can significantly improve the quality of treatment for patients with epilepsy [11; 12]. Epilepsy encompasses various categories determined by the nature of symptoms and the level of brain involvement, including etiology, type of seizure, and syndrome. Generalized epilepsy is characterized by epileptic waves that span the entire brain and cause widespread effects, whereas focal epilepsy is associated with epileptic waves that impact a specific region of the brain. However, it is important to note that the term \"epileptic waves\" is not well-defined and requires further clarification [13]. In addition, there is a parameter called SL which is used to detect linear and non-linear dependencies between brain signals, especially in theta wave. In this study, the performance of epilepsy predictors will be evaluated in terms of metrics such as sensitivity, specificity, and accuracy using two methods: one that employs wavelet transform and one without wavelet transform. Then, a new method will be developed to detect epilepsy using EEG signals. The predictors will be evaluated separately using both methods. Similar nearest neighbors (NNN) is one of the new clustering methods used in epilepsy diagnosis. This method can check the clusters with high accuracy; nowadays, much attention is paid to this method. Similar nearest neighbors has a high similarity to human brain function and hence can provide accurate results for epilepsy. People with epilepsy may have general seizures which affect the entire surface of the brain and may affect all areas of the body. Generalized-onset seizures are surges of abnormal nerve discharges throughout the cortex of the brain. The most common cause of generalized seizures is an imbalance in the inhibitory circuits and excitatory circuits of electrical activity in the brain. This study will follow these steps: first, a review of previous studies will be provided. Then, the data sets used in the study will be described. The proposed method, including preprocessing, signal processing, feature extraction, and classification, will be explained.