Epileptic Electroencephalogram Research Articles

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

A case of recurrent convulsions caused by acute sulfuryl fluoride gas poisoning

Sulfuryl fluoride is a kind of pesticide with strong permeability, convenient use at low temperature, non-corrosive and other characteristics, which can kill food pests and has strong lethality to termites. In acute sulfuryl fluoride poisoning, patients can see recurrent convulsions, epileptic electroencephalogram abnormalities such as matrix spikes or high amplitude spikes. In this paper, a patient with sulfuryl fluoride poisoning with convulsion-based mental system symptoms was reported, and after clinical treatment with dexamethasone and phenobarbital sodium, the patient was cured and discharged.

Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm.

Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance.

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Online Learning Koopman Operator for Closed-Loop Electrical Neurostimulation in Epilepsy.

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic electroencephalogram (EEG) with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

Dynamic analysis of epileptic causal brain networks based on directional transfer function

Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine.

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel-Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

Epileptic Spike Detection Using Neural Networks With Linear-Phase Convolutions

To cope with the lack of highly skilled professionals, machine learning with proper signal processing is key for establishing automated diagnostic-aid technologies with which to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with the appropriate passbands is essential for enhancing the biomarkers-such as epileptic spike waves-that are noted in the EEG. This paper introduces a novel class of neural networks (NNs) that have a bank of linear-phase finite impulse response filters at the first layer as a preprocessor that can behave as bandpass filters that extract biomarkers without destroying waveforms because of a linear-phase condition. Besides, the parameters of the filters are also data-driven. The proposed NNs were trained with a large amount of clinical EEG data, including 15833 epileptic spike waveforms recorded from 50 patients, and their labels were annotated by specialists. In the experiments, we compared three scenarios for the first layer: no preprocessing, discrete wavelet transform, and the proposed data-driven filters. The experimental results show that the trained data-driven filter bank with supervised learning behaves like multiple bandpass filters. In particular, the trained filter passed a frequency band of approximately 10-30 Hz. Moreover, the proposed method detected epileptic spikes, with the area under the receiver operating characteristic curve of 0.967 in the mean of 50 intersubject validations.

Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression

Seizures are a common symptom of this neurological condition, which is caused by the discharge of brain nerve cells at an excessively fast rate. Chaos, nonlinearity, and other nonlinearities are common features of scalp and intracranial Electroencephalogram (EEG) data recorded in clinics. EEG signals that aren't immediately evident are challenging to categories because of their complexity. The Gradient Boost Decision Tree (GBDT) classifier was used to classify the majority of the EEG signal segments automatically. According to this study, the Hurst exponent, in combination with AFA, is an efficient way to identify epileptic signals. As with any fractal analysis approach, there are problems and factors to keep in mind, such as identifying whether or not linear scaling areas are present. These signals were classified as either epileptic or non-epileptic by using a combination of GBDT and a Support Vector Regression (SVR). The combined method's identification accuracy was 98.23%. This study sheds light on the effectiveness of AFA feature extraction and GBDT classifiers in EEG classification. The findings can be utilized to develop theoretical guidance for the clinical identification and prediction of epileptic EEG signals. Doi: 10.28991/ESJ-2022-06-01-011 Full Text: PDF

A Unified Approach for Simultaneous Graph Learning and Blind Separation of Graph Signal Sources

Blind separation of the sources supported by graphs, i.e., graph signals, is a nascent and challenging Blind Source Separation (BSS) problem. In these cases, along with the statistical independence of the sources, additional dependency information can be interpreted from their graph structure, which provides exploiting different Graph Signal Processing (GSP) tools to improve the separation performance. To the best of our knowledge, in these cases, only GraDe and GraphJADE methods have been proposed to exploit the graph dependencies and/or GSP techniques to improve the separation quality rather than the conventional BSS methods. Despite the significant advantages of these graph-based methods, assuming that the underlying graphs are known is a serious drawback, especially in many real-world applications. To address this issue, in this paper, we propose a Unified objective function for GraphJADE with Graph Learning, namely U-GraphJADE-GL, and use the Block Coordinate Descent (BCD) to optimize it. In our proposed method, along with the separation task, the underlying graphs are learned simultaneously in an efficient manner. We compare the performance of the U-GraphJADE-GL with that of the GraDe with Graph Learning (U-GraDe-GL) and the conventional BSS methods in the BSS task and also analyze the GL performance. Besides, as well as the theoretical and experimental convergence analysis, we derive/state the Cramér-Rao bound (CRB) on the estimation of the mixing and unmixing matrices and also on the attainable Interference-to-Source Ratio (ISR) and compare the asymptotic performance of the proposed method with that of the optimal CRB estimators. To investigate the applicability in real applications, the performance of the proposed method is also compared with that of the abovementioned algorithms for denoising the epileptic Electroencephalogram (EEG) signals and also for the audio speech source separation task. The results show the superiority of the proposed algorithm compared to the other methods in the case of BSS of graph signals with unknown graphs.

Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

Neurophysiological features in patients with migraine at risk for epilepsy

Objective: to study the electrophysiological parameters of brain bioelectric activity and features of the autonomous nervous system assessing heart rate variability, sympathetic skin response, clinical and physiological tests depending on the lateralization of migraine pain syndrome in the right or left brain hemisphere in patients with epileptic electroencephalogram (EEG) signs and migraine.Material and methods. Thirty six patients with aura-free episodic migraine at risk of developing epilepsy and 9 age-matched healthy subjects were examined. All participants underwent EEG, clinical and physiological tests, assessment of heart rate variability and sympathetic skin response.Results. Patients with right-hemisphere migraine headache had signs of activated sympathetic nervous system at baseline level and during exercise, lower baseline EEG epileptiform activity and in provocative tests. In contrast to the subjects of this group, patients of other group featured with a more stable migraine pain syndrome in the left hemisphere tended to dominate with functional activity of the parasympathetic system such as increased trophotropic support during exercise, as well as greater magnitude of baseline epileptiform bioelectric activity and during load tests.Conclusion. Values of heart rate variability in combination with objective results of clinical and physiological assessment of the autonomous nervous system and electrophysiological parameters of brain bioelectric activity are reliable prognostic indicators for varying functional conditions in patients with episodic migraine.

Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal

High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is categorized as an epileptic signal or not (healthy). Several automated techniques have been proposed to assist neurologists in classifying these signals. In general, these techniques have yielded a high average accuracy in classification, but the performance still needs to be improved. Therefore, we propose a convolutional neural network based on multi-segment of EEG signals to classify epileptic EEG signals. This method is built to overcome data limitations in the convolutional neural network training process and add the ensemble combination process. The multi-segment of EEG signal is formed by splitting the signal without overlapping each channel and converting it into the spectrogram image based on the short-time Fourier transform value. The spectrogram image is then used as input for the convolutional neural network in in-depth training and testing. The convolutional neural network model of the training results is used to classify each EEG signal segment on each test channel before entering the ensemble combination stage for the final classification. To evaluate the performance of our proposed method, we used the Bonn EEG dataset. The dataset consists of five EEG records labelled as A, B, C, D, and E. The experiments on several datasets (AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E) which were arranged from the dataset showed that our proposed method (with segment) performs better than without segment. Our proposed method yielded the best average of classification accuracy which is 99.33%, 100%, 100%, 99.5%, 99.8%, and 99.4% for the AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E.By these results, the proposed method can outperform several other methods on the same dataset.

Automatic identification of epileptic seizure signal using optimized added kernel support vector machine (OAKSVM)

In this work, empirical mode decomposition (EMD)-based optimized added kernel least square support vector machine (OAKLSSVM) hybridized model is proposed for automatic identification of epileptic electroencephalogram (EEG) signals where the kernel parameters are being optimized using water cycle algorithm (WCA). The proposed model with EMD decomposition and WCA optimization together is known as EMD-OAKLSSVM-WCA. Here, two kernel functions i.e., radial basis function and wavelet kernel functions are deployed together to form the added kernel framework. From EMD, intrinsic mode functions (IMFs) are obtained where Hilbert transform (HT) is used to obtain analytic form of IMFs. For classifying seizure and non-seizure EEG signals, the frequency modulation bandwidth and amplitude modulation bandwidth parameters are obtained from the analytical IMFs and are used as features for the OAKLSSVM model. The experimental results validate the efficiency of the proposed model which provides better classification accuracy (99.33%) as compared to different promising classifiers and some state-of-the-art models.

Multilayer weighted integrated self‐learning algorithm for automatic diagnosis of epileptic electroencephalogram signals

AbstractEpilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95.

A novel peak signal feature segmentation process for epileptic seizure detection

Epilepsy is a brain disease in nerves which causes sudden seizure, sensations, and once in a while loss of mindfulness. This disorder is difficult to find manually because of its unpredictable nature since it is very hard to treat. The World Health Organization states that fifty million people having this type of disorder worldwide. Automatic detection assumes a significant role in the finding of epilepsy for it can get imperceptible data of Epileptic Electroencephalogram Signals precisely and diminish the burdens of medical field. The Brain’s function is monitored by using these EEG signals electrically. The goal of this paper is to find a classification on Electroencephalogram (EEG) signals using the Bonn University datasets. In order to address this challenge, we propose a new Peak Signal Features (PSF) method which extracts high and low peak features from EEG signals. In addition, Support Vector Machine, Decision Tree and K-Nearest Neighbor are used for classification. Finally, overall accuracy and the Mean Square Error rates of the above three classification methods with proposed method are measured. The experimental result demonstrates the effectiveness of the proposed approach. It also proves that SVM with proposed Peak Signal Features method gives better result than the other methods.

Phosphorylation of PRAS40 contributes to the activation of the PI3K/AKT/mTOR signaling pathway and the inhibition of autophagy following status epilepticus in rats.

Status epilepticus (SE) is a neurological disorder associated with high morbidity and mortality rates, and is often difficult to treat. Moreover, the underlying mechanism of SE remains unknown. The lithium-pilocarpine model is a validated animal model that can reproduce the main clinical and neuropathological features of SE. In the present study, this SE model was utilized and SE was successfully established in rats, as determined by the corresponding epileptic electroencephalogram. Histology, immunohistochemistry, western blot analysis and co-immunoprecipitation were used to detect the phosphorylation (p-) of AKT substrate of 40 kDa (PRAS40), the combination of p-PRAS40 and 14-3-3 protein and the activation of the PI3K/mTOR signaling pathway in SE. In addition, the present study analyzed the dynamics of the expression of autophagy-associated factors in the hippocampus after SE induction, and the influence of suppressing the p- of PRAS40 on the autophagy process was detected in the pathogenesis of SE. The results indicated that increased p-PRAS40 expression could activate the mTOR pathway to decrease the level of autophagy. However, inhibition of the mTOR signaling pathway promoted autophagy flux. These results may provide further understanding of p-PRAS40 functions in SE.

Identification of Epileptic Electroencephalograms Signals Using an Integrated Transfer Radius Basis Function Neural Network

As a common brain disease, epilepsy is rapidly increasing in terms of the number of patients. Long-term repeated sudden seizures seriously affect the physical and mental health of patients. Epileptic electroencephalogram (EEG) signals are an effective tool in the hands of clinicians for diagnosing epilepsy, and how to use computer technology to automatically analyze and detect epileptic EEG signals has become very meaningful. This article proposes a method for effectively identifying epileptic EEGs for further diagnosis of epilepsy. The traditional modeling method default is to train on training samples and test samples that obey the same distribution, which usually does not match the actual situation. Therefore, a transfer learning (TL) mechanism is introduced to a classical radial basis function neural network (RBFNN). Considering the limited stability of a single classifier, this article introduces an integration strategy and proposes an integrated transfer RBFNN (ITRBFNN) algorithm. Experimental results of EEG signal recognition for epilepsy show that the algorithm has better adaptability of scene transfer and stability.

A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal

This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG). Signals used in this work are 23.6 s segments of 100 single channel surface EEG recordings collected with the sampling rate of 173.61 Hz. The recorded signals are from five healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from five epilepsy patients during the seizure-free interval as well as epileptic seizures. Feature engineering was done using; i) feature extraction of each EEG wave in time, frequency and time-frequency domains via Butterworth filter, Fourier Transform and Wavelet Transform respectively and, ii) feature selection with T-test, and Sequential Forward Floating Selection (SFFS). SVM and KNN learning algorithms were applied to classify preprocessed EEG signal. Performance comparison was based on Accuracy, Sensitivity and Specificity. Our experiments showed that SVM has a slight edge over KNN.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG.

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.