New Epilepsy Research Has Been Reported by Researchers at School of Electronics Engineering (Seizure detection in EEG signal using Gaussian-stockwell transform and Hermite polynomial features).

Researchers at the School of Electronics Engineering in Vellore, India have developed a method for detecting seizures in electroencephalography (EEG) signals using the Gaussian-stockwell transform (GST) and Hermite polynomial features. The researchers applied these features to the Random Forest Classifier (RFC) algorithm and achieved an optimal classification accuracy of 96.4%, with a sensitivity of 97% and a specificity of 96.4%. This research demonstrates the effectiveness of the proposed method in distinguishing seizure activity in EEG signals. [Extracted from the article]