Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

SIMPLE SUMMARY: Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). ABSTRACT: Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.