Narrow Results

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including $k$-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

Feature extraction is an essential procedure in the detection and recognition of epilepsy, especially for clinical applications. As a type of multichannel signal, the association between all of the channels in EEG samples can be further utilized. To implement the classification of epileptic seizures from the nonseizures in EEG samples, one graph convolutional neural network (GCNN)-based framework is proposed for capturing the spatial enhanced pattern of multichannel signals to characterize the behavior of EEG activity, which is capable of visualizing the salient regions in each sequence of EEG samples. Meanwhile, the presented GCNN could be exploited to discriminate normal, ictal and interictal EEGs as a novel classifier. To evaluate the proposed approach, comparison experiments were conducted between state-of-the-art techniques and ours. From the experimental results, we found that for ictal and interictal EEG signal discrimination, the presented approach can achieve a sensitivity of 98.33%, specificity of 99.19% and accuracy of 98.38%.

A discrete model of a neural network of excitatory and inhibitory neurons is presented which yields oscillations of its global activity. Different types of dynamics occur depending on the selection of parameters: oscillating population activity as well as randomly fluctuating but mainly constant activity. For certain sets of parameters the model also shows temporary transitions from apparently random to periodic behavior in one run, similar to an epileptic seizure.

Studies have shown that inhibitory interneurons include inhibitory interneurons on fast time scales (I1) and inhibitory interneurons on slow time scales (I2), both of which can modulate epileptic activities. Based on this, we improve the corticothalamic model by introducing self-inhibition of I1 and I2 to explore the onset and transitions of epileptic activities from the dynamic perspective. First, we investigate the case that the excitatory loop between the excitatory pyramidal neurons (PY) and the thalamic relay neurons (TC) induces epileptic activities in the absence of self-inhibition. The emergence of multistability drives the system to produce a variety of epileptic activities, such as m-spike-wave discharges, tonic and clonic oscillations. Subsequently, we introduce self-inhibition of inhibitory interneurons and find that the advancement or disappearance of the Hopf bifurcation leads to a reduction in the area of epileptic discharges. Finally, we explore epileptic activities induced by the self-inhibition of I1 and the excitability of TC on I1. The strong self-inhibition can effectively modulate seizures, but excessive exchange of information between TC and I1 induces absence seizures. In addition, the paper focuses on revealing the loop mechanisms and related bifurcation characteristics in various situations. Besides Hopf and fold limit cycle bifurcations, period-doubling and homoclinic bifurcations are also key factors leading to the state transitions. These may provide some theoretical guidance for the generation and evolution of seizures.

We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg–Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively.

In this study, we investigate multiscale entropy (MSE) as a tool to evaluate the dynamic characteristics of electroencephalogram (EEG) during seizure-free, pre-seizure and seizure state, respectively, in epileptic rats. The results show that MSE method is able to reveal that EEG signals are more complex in seizure-free state than in seizure state, and can successfully distinguish among different seizure states. The classification ability of the MSE measures is tested using the linear discriminant analysis (LDA). Test results confirm that the classification accuracy of MSE method is superior to traditional single-scale entropy method. MSE method has potential in classifying the epileptic EEG signals.