(1) Bonn dataset has been used in the experiment is from the EEG database of the Epilepsy Research Center of the University of Bonn, Germany. (2) CHB-MIT was recorded from Boston Childrens Hospital, which collected continuous, long-term and multi-channel EEG signals from 23 pediatric patients at a 256Hz sampling rate. CHB-MIT dataset is available from https://physionet.org/content/chbmit/1.0.0/.
GLCM and LBP descriptors were used to extract the global and local features of time-frequency images respectively.
HHO code has be publicly proviede in https://aliasgharheidari.com/HHO.html
A key novelty of this work is to introduce two search strategies on the original HHO, namely, hierarchy mechanism and transfer function. The former can divide the population into hierarchical structure to enhance the local search ability of HHO algorithm and avoid falling into local optimum. The latter can enhance the diversity of the population and accelerate the rate of convergence in the search phase of the population.
The k-nearest neighbor (KNN) classifier is used in this paper to classify epileptic EEG signals.