# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 5 Discussion  
As previously mentioned, the causes of epileptic seizures include epidemiology, etiology, acute stroke, brain infection, and the adverse effects of certain medications. In previous studies, some researchers have used a combination of EEG signals with FMRI to detect physiological values [9; 10]. In recent years, advanced methods focusing on functional dynamics have been proposed in studies to assist people with epilepsy. For example, the SL parameter in the theta frequency band has been studied in various studies of individuals with epilepsy and has been shown to classify epilepsy with a sensitivity of 62% and a specificity of 76%. As previously noticed, the SL parameter is used to detect linear and non-linear dependencies between brain signals, especially in theta wave. In addition, this parameter can determine sudden attacks in epilepsy and is therefore very important. The techniques for analyzing brain signals have been improved in recent years as they can reflect brain activity but we still need more improved techniques to obtain more accurate results. The aim of this study was to demonstrate the predictive value of physiological signals measured using EEG, which was conducted in two different experimental stages. By testing on the Bonn data sets, as can be seen in Figure 1 and according to the explanation in the Section 3, each recording data composed of the following: (a) non-epileptic recording with open eyes, (b) non-epileptic recording with closed eyes, (c) recordings of EEG from the hippocampal formation in the hemisphere opposite the epileptogenic zone, (d) comprised EEG recordings of the epileptogenic zone, and finally (e) a collection of epileptic seizure activity recorded from the hippocampal focus. This study demonstrated the effectiveness of the proposed method compared to recent studies and highlighted how a predictive model based on EEG characteristics can significantly improve clinical diagnosis and contribute to diagnosis of epileptic symptoms in the EEG signal.  
As you can see in Table 5, the results obtained using the proposed methods have been more accurate compared to other methods. The use of deep learning methods as well as wavelet transform techniques and the use of four levels of decomposition have led to improved results in this study. This study has shown that the use of these methods of wavelet transform and Shannon entropy in the diagnosis of epilepsy can be very effective and lead to an easier diagnosis of the disease and its control by neuroscientists.  
In the first step, using a multilayer perceptron neural network (MLP) classifier, epilepsy and control signals in the alpha, beta, and gamma frequency bands were analyzed separately and accuracy equal to 95% was achieved in detecting the epileptic signal. Then, using a wavelet transform function, the signal in the alpha, beta, and gamma frequency bands was re-examined and accuracy equal to 91% was achieved in detecting the epileptic signal. This study has succeeded in developing a simpler method for assessing epilepsy from EEG signals and also achieved satisfactory parameters, such as accuracy, compared to other studies.