# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 2 Related Studies  
This article highlights several noteworthy studies in the field of epileptic seizures, focusing on their significance and relevance. [PERSON] et al. [14] have proposed a method for automatic seizure detection in EEG recordings using the discrete wavelet transform (DWT) and support vector machines (SVM). The method consisted of four steps: segmentation, wavelet analysis, feature extraction, and classification. In the first step, the long-term EEG recording was divided into 2 s windows. Then, five-level wavelet transform was applied to each window, dividing the signal into several frequency sub-bands. In the next step, five features were extracted from each sub-band and used to train an SVM classifier. The classifier was then used to classify the sub-bands as either seizure or non-seizure activity. In the study by [PERSON] and [PERSON] et al. [15], two techniques using SVM were employed to classify EEG signals as convulsive or non-convulsive. The first technique, called sub-pattern-based principal component analysis (SpPCA), involved dividing the input patterns into subsets and extracting features from each subset using principal component analysis (PCA). The extracted features were then combined according to the pattern partition sequence to form the final feature vectors. The second technique involved performing PCA on the features extracted in the first step to further reduce the dimensionality and extract overall features. In the study by [PERSON] et al. [16], they used SVM to diagnose epileptic seizures using EEG data. Their approach achieved an accuracy of 96% when tested on experimental data. [PERSON] et al. [17] used a third-order cumulant function to automatically detect focal EEG signals. They extracted features from the EEG signals using locality sensitive discriminant analysis (LSDA) and then SVM to classify these features. The authors obtained a maximum classification accuracy of 99% using the Bern-Barcelona EEG data set. [PERSON] et al. [18] proposed a method to distinguish between focal and non-focal EEG signals using sliding mode singular spectrum analysis. The authors calculated features from the reconstructed component of the EEG signal and used a radial basis function neural network to classify them. When tested on the Barcelona EEG data set, the method achieved an average accuracy of 99.11%, average sensitivity of 98.52%, and average specificity of 99.7%. [PERSON] et al. [19] proposed a method to extract 25 features of the EEG signal using the LSDA method. These features were then classified using an SVM classification approach. The authors tested the method using the cross-validation approach on the Barcelona EEG data set, achieving a classification accuracy of 96.2%. [PERSON] et al. [20] proposed an EEG-based computer diagnostic method for epilepsy using a combination of nonlinear filters and an artificial neuron network (ANN). The proposed method achieved an accuracy of 97.2%. [PERSON] et al. [21] compared various entropy algorithms and found that entropy values could be used to distinguish between normal and epileptic EEG. They used an adaptive neuro-fuzzy inference system (ANFIS) for classification and achieved 92.2% precision. [PERSON] et al. [22] used an adaptive neural fuzzy network and the energy of discrete wavelet transform (DWT) sub-bands for epilepsy diagnosis. However, their proposed method had a low accuracy of approximately 85.9%. [PERSON] et al. [23] studied the use of the flexible analytic wavelet transform (FAWT) to decompose EEG signals. This non-stationary transform generated fractal dimension features at each scale level. The proposed method was tested using the Kruskal-Wallis statistical test, and the classification accuracy was 89.1% on the Barcelona EEG data set. [PERSON] et al. [24] analyzed a soft computing-based adaptive neuro-fuzzy inference system (ANFIS) classification method and used the feature values to distinguish between focal and non-focal signals.