Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model

Electroencephalogram (EEG) recordings are analyzed to make a diagnosis of neurological diseases like epilepsy before surgical intervention. It is essential to examine the EEG signals of individuals with epilepsy to separate between focal and non-focal seizure sources. In this research, a train-optimized hidden Markov model (HMM) based on human learning optimization is proposed to automatically detect the epileptic seizure by distinguishing the focal and non-focal epileptic EEG signals. Human learning optimization (HLO) plays a crucial part in electrode selection, which aids in the subsequent feature extraction process, reduces the model's complexity, and chooses the features with the best discriminating properties. The HLO-train optimized HMM model receives the feature vector made up of frequency band-based features, which is then tuned to optimum using the proposed model to improve detection performance. The searching and learning skills of the human is included in the development of the HLO algorithm, that helps in boosting the convergence of the model adds value to the research. The Epileptic seizure is effectively recognized using the model and the efficiency of the research is proved by measuring the evaluation metrices. The HLO-train optimized HMM model attained for the CHB-MIT scalp EEG database with an accuracy of 99.158 %, sensitivity of 98.086 %, and specificity of 99.155 %, while the accuracy of 94.231 %, sensitivity of 92.075 %, and specificity of 99.926 % with the Siena scalp dataset, which is efficient, when compared with state of art methods.