Abstract:
Electroencephalogram (EEG) is progressively developing as a remarkable structure of neuron action. It comprises of
massive information that is used for identifying abnormality and dealing with intellectual disorders and irregularities. Present paper
shows study related to EEGs of abnormal subjects and those are analyzed with respect to normal subject. Numerous topographies
like Mean, Entropy, Wavelet bands are evaluated and compared. Building upon the adaptive hunting strategies observed in coyotes,
this hybrid computational model is fused with deep learning architectures to enhance diagnostic accuracy. The methodology involves
the creation of a unique computational algorithm inspired by coyote hunting behaviors, integrated with deep neural networks. This
hybrid model is applied to analyze EEG data for brain disorder detection, leveraging both the biological-inspired algorithm and the
data-driven capabilities of deep learning. Regarding the results, the proposed scheme exhibits promising diagnostic accuracy,
achieving an accuracy rate of 98.65% for training (True Positive - TP) and 98.82% utilizing k-fold validation. These preliminary
results demonstrate the potential effectiveness of the hybrid approach in accurately detecting brain disorders from EEG signals.
However, it's important to note that these results are indicative of the initial success and represent a part of the comprehensive
evaluation conducted in this study.
