Epileptic EEG Classification Research Articles

EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection.

Diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG) abnormalities is an inefficient, time-consuming, and expert-centered process. Moreover, the diagnosis based on ictal epileptiform events is challenging as the ictal patterns are infrequent. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. The interictal epileptiform discharges (IEDs) are recurring patterns that are highly suggestive of epilepsy. In this paper, we propose an epileptic EEG classification system based on IED detection. The proposed system comprises of three modules: pre-processing, waveform-level classification, and EEG-level classification. We employ a Convolutional Neural Network (CNN) for waveform-level classification and a Support Vector Machine (SVM) for EEG-level classification. We evaluated the proposed system on a dataset of 156 EEGs recorded at Massachusetts General Hospital (MGH), Boston. The system achieved a mean 4-fold classification accuracy of 83.86% for classifying EEGs with and without IEDs.

Performance analysis of power and power variance for classification, detection and localization of epileptic multi-channel EEG

There are a large number of data sets of EEG signal for which, it is difficult to judge and monitor brain activity through observations. Epilepsy is a disorder in which a recurrent and sudden malfunction of the brain is characterized. It is proposed to classify, detect and localize Epileptic multi-channel EEG through various power and novel power variance features non-invasively. This work presents power spectral estimation (PSE) using time–frequency analysis of EEG signals in both parametric (FFT) and non-parametric methods (i.e. Welch, Burg, Covariance, MUSIC and Yule–Walker). To examine the robustness of power features for different methods, the analysis of p value is performed. The detection of epileptic seizure is classified using different kernels through SVM. It is observed from the PSE that the power features have higher values in epileptic subjects as compared to non-epileptic subjects. Amongst all the parametric and non-parametric methods, the MUSIC method gives the highest average power. Sensitivity, specificity, and classification accuracy are 100% for Welch, Burg, Covariance, and Yule–Walker methods while MUSIC and FFT methods deliver 98.73 and 99.52% respectively. The novelty is introduced through the quantification of power and power variance robust feature region/lobe-wise. This quantification is used for the localization of 25 epileptic subjects. Analysis of the parametric and non-parametric PSD methods for extraction of power and power variance features is not used by any study. These are effectively utilized for detection and localization of epilepsy non-invasively.

Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring

Long-term electroencephalogram (EEG) monitoring is effective for epilepsy diagnosis. However, it also takes a lot of time for clinicians to correctly interpret the long-time recordings. Real-time computer-based EEG monitoring and classification systems have attracted recently the attention of researchers to help clinicians locate online possible epileptic-form EEG signals. In this paper, we first present an accurate and fast EEG classification algorithm that can recognize three types of EEG signals: normal, spike, and seizure. 16-channel bipolar EEG recordings of epilepsy patients are preprocessed, segmented, and ensemble empirical mode decomposed (EEMD) into intrinsic mode functions (IMFs). Features are extracted and linear discriminant analysis (LDA) is applied to train two classifiers: one is for seizure and non-seizure discrimination, and the other is for normal and spike discrimination. In order to furthermore help the clinicians, the results of LDA are visualized and sonified. The changes of the discriminant in the LDA on continuous EEG segments are backtracked to each feature, and thus to each EEG channel. Accordingly, contours of the changes in EEG channels are depicted. At the same time, sinusoidal waves in 440 or 880 Hz are played when EEG segments are classified into spike or seizure respectively. In the experiment, EEG recordings of six subjects (two normal and four seizure patients) are examined. The experiment result shows that the accuracy of the proposed epileptic EEG classification algorithm is relatively high. In addition, the visualization and sonification algorithms of epileptic-form EEG may greatly help clinicians localize the focus of seizure and nurses take care of seizure patients, immediately.

Epileptic electroencephalogram recognition based on discrete S transform and permutation entropy

Electroencephalogram(EEG) analysis has important reference value in the diagnosis of epilepsy. The automatic classification of epileptic EEG can be used to judge the patient's situation in time,which is of great significance in clinical application. In order to solve the problem that the recognition accuracy is not high by using the single feature of EEG signals and avoid the influence of wavelet basis function selection on recognition results,a method of automatic discrimination of epileptic EEG signals based on S transform and permutation entropy is proposed. Firstly, the original signals are decomposed by discrete S transform, and then we calculate the fluctuation index of coefficients of each rhythm and combine the permutation entropy of EEG signals into a feature vector and use Real AdaBoost classifier to discriminate the epileptic EEG signals in muti-period. In this study, we used the epilepsy database from University of Bonn. Three groups of EEG signals, including the data of normal people with their eyes open, the data collected inside of the epileptic foci from patients during their interictal period and the data during their ictal period, were used to test effectiveness. The results of this study showed that the fluctuation index of each rhythm could be used to characterize the normal, interictal and ictal epileptic EEG signals effectively, and the recognition accuracy of multiple features was much higher than that of single feature. The average recognition accuracy could reach 98.13%. Compared with time-frequency feature extraction method or nonlinear feature extraction method only,the recognition accuracy was increased by more than 1.2% and 8.1% respectively, which was superior to the methods recorded in many other literatures. Therefore, this method has a good application prospect in diagnosis of epilepsy.

Long-term epileptic EEG classification via 2D mapping and textural features

Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.

Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Support Vector Machine

Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Support Vector Machine

Epileptic EEG classification based on extreme learning machine and nonlinear features

The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are employed to characterize interictal and ictal EEGs. The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals.

Automatic feature extraction using genetic programming: An application to epileptic EEG classification

This paper applies genetic programming (GP) to perform automatic feature extraction from original feature database with the aim of improving the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of the original features.