Epileptic EEG signal classifications based on DT-CWT and SVM classifier

Contamination in human cerebrum causes the mind issue, which is epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs, and the other sound locales in the mind produce the standard example signals as nonfocal sign. Henceforth, the discovery of focal signs from the nonfocal signs is significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for Electroencephalogram (EEG) signals orders utilizing Support Vector Machine (SVM) classifier. The exhibition of the proposed EEG signals characterization framework is assessed for Sensitivity, Specificity, and Accuracy.

Automated summary

This paper proposes a simple and efficient method for classifying EEG signals using a Support Vector Machine classifier, and evaluates the performance of the proposed EEG signals classification system in terms of Sensitivity, Specificity, and Accuracy.