Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification.

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]