Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

Background: Epilepsy is a Brain disorder disease that affects people's quality of life. If it is diagnosed at an early stage, it will not be spread. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. However, this screening system cannot diagnose epileptic seizure states precisely. Nevertheless, with the help of computer-aided diagnosis systems (CADS), neurologists can diagnose epileptic seizure stages properly. Objective:The aims of this study are to epileptic seizures diagnosis by using EEG signals and distinguish its different stages. Using various statistical and non-linear features, the CADS proposed in this study is capable of diagnosing epilepsy seizures precisely and quickly. Therefore, this system can help the neurologists to diagnose more accurately. Material and methods: This paper focuses on a novel method for epileptic seizure detection based on EEG signals using artificial intelligence (AI) techniques. First, the Bonn dataset is used for trials, and the EEG signals are divided into 5-second intervals. Then, tunable q-factor wavelet transform (TQWT) was utilized to analyze EEG signals into various sub-bands. Several statistical and nonlinear features (fractal dimensions (FDs) and entropies) of TQWT sub-bands are extracted in the following sections. In the next procedure, the Autoencoder (AE) method with proposed layers is applied to reduce features, and finally, different classification algorithms such as Support Vector Machine (SVM), SVM with grasshopper optimization algorithm (SVM-GOA), K-Nearest Neighbors (KNN), and Random Forest (RF) are employed. The employment of AE for feature reduction and SVM-GOA for classification is the novelty of this study. Results: According to results, the proposed method of epileptic seizure detection demonstrated better performance compared to related works. The proposed SVM-GOA classification method has a higher accuracy rate as much as 99.42% and 99.23% for two-class and multi-class classification problems, respectively. Conclusion: The combination of effective features in diagnosing periods of epileptic seizure along with appropriate classification methods increases the accuracy of the CADS. Considering the importance of diagnosing various epileptic seizures, a high-precision CADS is introduced in this work. High accuracy, using different methods for extracting features and classification are among the advantages of our proposed method.