Hybrid coyote predator with DL network for brain disorder detection using EEG signals

Abstract

ABSTRACT Brain abnormality is one of the dominant health issues in recent years, and electroencephalogram (EEG) is a mostly employed method for identifying abnormalities in the brain. Brain abnormalities can be cured only when it is detected early stage. Even though there are various techniques for the detection of brain abnormalities, the health sector concentrates more on the advanced modern method to diagnose the abnormality very precisely. Advanced DL methods have attracted physicians very much due to the fascination with developing detection methods with minimal errors. Hence, this paper deeply concentrated on developing a well-structured technique to predict brain abnormalities using the hybrid coyote predator optimisation-dependent DCNN (hybrid coyote predator-DCNN). The results of this study demonstrate the value of hybrid coyote predator optimisation, which inherits qualities like a long jump and an intelligence ability to adapt to its surroundings and aids in fine-tuning the modal parameters of the DCNN. Additionally, to improve the detection model’s precision, the frequency bands from which the features are taken are separated to identify brain changes. Utilising the CAP dataset and the performance metrics, the analysis is carried out. Regarding the TP and k-fold, the hybrid coyote predator-DCNN model attained accuracy of 98.446% and 97.384%, respectively.