Epileptic Electroencephalogram Signals 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.

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.

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.

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.

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi-kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an efficient optimisation algorithm known as water cycle algorithm (WCA) for diagnosis and classification of epileptic electroencephalogram (EEG) signals. The proposed model with optimisation is known as WCA–EMD–WMKRVFLN. The tanh and wavelet kernel functions are contributing together to the effectiveness of the proposed model. The features generated from EMD in terms of intrinsic mode functions (IMFs) are modulated to find important statistical and entropy based features and these features in a reduced form are employed as inputs to the model to classify epileptic EEG signals. The presented approach is evaluated in terms of percentage correct classification accuracy (ACC), specificity and sensitivity using two datasets and is compared with different classifiers and state-of-the-art techniques. The highest accuracies of 99.69% (five-class) and 100% (three-class) achieved using the Bonn-University dataset and 99.0% ACC (two-class) achieved using the Bern-Barcelona dataset. The achieved results report that the presented approach is a promising approach for EEG signal classification and is superior to several state-of-the-art techniques and is highly comparable to many such techniques.

Classification of epileptic electroencephalogram signals using tunable-Q wavelet transform based filter-bank

The epilepsy is a neurological disorder and the seizure events frequently appear in epileptic patients. This disorder can be analysed through electroencephalogram (EEG) signals. In this paper, we propose a novel approach for automated identification of seizure EEG signals. The proposed method in this paper decomposes EEG signal into set of sub-band signals by applying tunable-Q wavelet transform (TQWT) based filter-bank. The sub-bands in TQWT based filter-bank have different value of quality (Q)-factor and have nearly constant bandwidth (BW). The features are computed by applying cross-information potential (CIP) on $$N_s$$ number of sub-band signals, for $$N_s$$ values varying from two to maximum number of sub-band signals obtained from TQWT based filter-bank. The features are computed for various values of $$N_s$$ and fed as input to random forest (RF) classifier. We have observed that, with the increase in the $$N_s$$, the number of computed features increases and hence the classification accuracy (ACC) depends on $$N_s$$. In this work, we have obtained ACC of $$99 \%$$ in the classification of normal, seizure-free, and seizure EEG signals using our proposed method. The developed algorithm is ready to be tested using huge database and can be employed to aid the epileptologists to screen the seizure-free and seizure patients accurately.

Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal

Epilepsy is major challenge to medical fraternity. This research article has come up with a novel method for pre-processing of Epileptic Electroencephalogram (EEG) signals for artifact removal and localizing the epileptic seizure onset zone. The presence of physiological artifacts alters the true EEG information which can considerably affect the epileptic seizure classification accuracy. Hence, it is of prime importance to eliminate these contaminations. The analysis of epileptic EEG signal requires localization of the seizure onset zone in the epileptic EEG signal. The objective of this paper is to present an approach to eliminate physiological artifacts and to localize the epileptic region. This paper suggests a hybrid model based on Multiresolutional Analysis and Adaptive Filtering [MRAF]. Initially the EEG signal is decomposed using Discrete Wavelet Transform (DWT) which effectively helps to localize the epileptic region. Multiresolutional soft thresholding is applied to these decomposed wavelet components to remove the abrupt variations. Further, adaptive filtering is applied to remove the low frequency components which represents physiological artifacts. It is observed that the MRAF method effectively eliminate physiological artifacts and localizes the epileptic seizure zone present in the seizure EEG information. The accuracy of the proposed MRAF model is found to be 86.66% with a precision of 88.88%, since it is able to retain most of the seizure signal present in the tested datasets.

Analysis of epileptic seizure detection method based on improved genetic algorithm optimization back propagation neural network

In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.

Epileptic EEG Identification via LBP Operators on Wavelet Coefficients.

The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals. After that, the "uniform" LBP operator is carried out on the wavelet-based time-frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the "uniform" LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

This study proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic electroencephalogram (EEG) signals. This study employs the OA to determine representative sample points from the original EEG data and then applies periodogram (PD), autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k -nearest neighbour (k-NN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus, the findings of this study prove that the proposed OA-based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.

A Classification Method for Epileptic Electroencephalogram Based on Wavelet Multi-scale Analysis and Particle Swarm Optimization Algorithm

The automatic classification of epileptic electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. In this paper, an epileptic EEG classification method based on wavelet multi-scale analysis and particle swarm optimization is proposed. Firstly, the multi-scale is carried out to the original EEG to extract its sub-bands of different frequency. Secondly, the Hurst exponent and the sample entropy are used to extract the EEG signals and its sub-bands. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the extreme learning machine (ELM), and the obtained eigenvector is put to PSO-ELM to realize the purpose of classification of epileptic EEG. The proposed method in this paper achieved 99.7% classification accuracy for the discrimination between epileptic ictal and interictal EEG, which is superior to those methods in other studies.

Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals.

Electroencephalogram (EEG) signal identification based on intelligent models is an important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional intelligent methods usually assume that the training dataset and testing dataset have the same distribution, and the data available for training are adequate. However, these two conditions cannot always be met in practice, which reduces the ability of the intelligent recognition model obtained in detecting epileptic EEG signals. To overcome this issue, an effective strategy is to introduce transfer learning in the construction of the intelligent models, where knowledge is learned from the related scenes (source domains) to enhance the performance of model trained in the current scene (target domain). Although transfer learning has been used in EEG signal identification, many existing transfer learning techniques are designed only for a specific intelligent model, which limit their applicability to other classical intelligent models. To extend the scope of application, the generalized hidden-mapping transductive learning method is proposed to realize transfer learning for several classical intelligent models, including feedforward neural networks, fuzzy systems, and kernelized linear models. These intelligent models can be trained effectively by the proposed method even though the data available are insufficient for model training, and the generalization abilities of the trained model is also enhanced by transductive learning. A number of experiments are carried out to demonstrate the effectiveness of the proposed method in epileptic EEG recognition. The results show that the method is highly competitive or superior to some existing state-of-the-art methods.

Recognition of Epileptic EEG Signals Using a Novel Multiview TSK Fuzzy System

Recognition of epileptic electroencephalogram (EEG) signals using machine learning techniques is becoming popular. In general, the construction of intelligent epileptic EEG recognition system involves two steps. First, an appropriate feature extraction method is applied to obtain representative features from the original raw EEG signals. Second, an effective intelligent model is trained based on the extracted features. However, there exist two major challenges in the process: 1) it is nontrivial to determine the appropriate feature extraction method to be used; 2) although many classical machine learning methods have been used for epileptic EEG recognition, most of them are “black box” approaches and more interpretable methods are desirable. To address these two challenges, a new epileptic EEG recognition method based on a multiview learning framework and fuzzy system modeling is proposed. First, multiview EEG data are generated by employing different feature extraction methods to obtain the features from different views of the signals. Second, the classical Takagi-Sugeno-Kang fuzzy system (TSK-FS) is introduced as an easy-to-interpret recognition model to develop a multiview TSK-FS method, called MV-TSK-FS, to identify epileptic EEG signals. For the proposed MV-TSK-FS, the importance of each view, i.e., the importance of each feature extraction method, can be evaluated according to the weighting of each view, and consequently the final decision can be made based on the weighted outputs of different views. Experimental results indicate that the MV-TSK-FS is a promising method when compared with the state-of-the-art algorithms.

Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals

The intelligent recognition of electroencephalogram (EEG) signals has become an important approach to the detection of epilepsy. Among existing intelligent identification methods, fuzzy logic systems (FLSs) have shown a distinctive advantage in identifying epileptic EEG signals because of their strong learning abilities and interpretability. Like many conventional intelligent methods for recognizing EEG signals, in the training of FLS, it is assumed that the training dataset and test dataset are drawn from data that are identically distributed. However, this assumption is not necessarily valid in practice as it is not uncommon for the two datasets to have different distributions. To overcome this problem, a strategy is presented in this paper to construct a Takagi–Sugeno–Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed for the training of the TSK FLS. As regression and binary classification are two common approaches to multiclass classification, the TSK transfer learning FLS algorithms for regression and binary classification are developed, respectively, to construct the corresponding TSK FLS. Both algorithms are further used to perform a multiclass classification to recognize epileptic EEG signals. Their performance in the epileptic EEG datasets indicates promise in dealing with situations where the training and test datasets differ with regard to data distribution.

Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.