Abstract
•Propose an ADLBSC-ESD model for brain signal classification and seizure detection.•Propose an efficient feature selection technique using an ITLBO algorithm.•Design a hyperparameter tuned DBN model for the classification of brain signals.•Validate the classifier results on benchmark Epileptic seizure recognition dataset.
Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively.