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In this paper, the efficiently extracted and reduced features using deep long short-term memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel random vector functional link net (MVKRVFLN) classifier are used to identify the seizure and non-seizure productively. Our methodology uses Children Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multi-channel scalp EEG data, Bonn university single-channel intracranial EEG data, and two-channel Bern Barcelona intracranial EEG data recordings to assess the performance. The non-stationary, non-linear, complex, and chaotic type EEG signal is directly applied to DLSTM to obtain compressed significant features. The scatter plots of the DLSTM output features signify this compressed information are unique in nature. The excellent generalization ability, faster learning rate, simpler network-based MVKRVFLN classifier is formulated to well identify the seizure and non-seizure epochs precisely by applying the deep LSTM extracted discriminative features as input. The type of kernel function selection and choice of regularization coefficient are added information to improve the performance of the proposed approach. The suggested technique provides excellent classification accuracy, superior detection ability, faster speed, and insignificant false positive rate per hour, simpler structure, robustness to classify the seizure and non-seizure signals. [Display omitted] • Deep LSTM is used to extract and reduce the number of features from EEG signals. • A new minimum variance KRVFLN classifier with LSTM is used for EEG classification. • The scatter plots of Deep LSTM features signify the compressed unique information. • This new classifier rejects outliers and noise and produces significant accuracy. • A proper choice of kernel to provide generalization and accuracy is proposed. [ABSTRACT FROM AUTHOR]

To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice. [ABSTRACT FROM AUTHOR]