Performance Exploration of Multiple Classifiers with Grid Search Hyperparameter Tuning for Detecting Epileptic Seizures from EEG Signals
This study evaluates the performance of two-level classifications using dimensionality reduction methods to determine the risk level of epilepsy from EEG dataset. To diminish the complexity of EEG data, dimensionality reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA), and Principal Component Analysis (PCA) are utilized. The risk level of epilepsy classification from EEG dataset would then be carried out using three classifiers: Hidden Markov Model (HMM), Naïve Bayesian Classifier (NBC) and Gaussian Mixture Model (GMM). The Grid Search (GS) process is employed to tune the hyperparameters of GMM and NBC classifiers. This study analyzed twenty patients’ datasets. Performance evaluation of classifiers with and without GS hyperparameter tuning is examined, including performance index, sensitivity, specificity, and accuracy. The GMM classifier with the GS hyper-tuning approach for SVD dimensionality reduction technique achieved a higher accuracy of 98.18% than its counterpart classifiers.
Epilepsy, GMM, Grid search, HMM, Hyperparameters
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