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Performance Exploration of Multiple Classifiers with Grid Search Hyperparameter Tuning for Detecting Epileptic Seizures from EEG Signals

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Title Performance Exploration of Multiple Classifiers with Grid Search Hyperparameter Tuning for Detecting Epileptic Seizures from EEG Signals
 
Creator Babu, C Ganesh
Shankar, M Gowri
Rajaguru, Harikumar
 
Subject Epilepsy
GMM
Grid search
HMM
Hyperparameters
 
Description 754-766
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.
 
Date 2022-07-06T06:50:55Z
2022-07-06T06:50:55Z
2022-07
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/60055
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(07) [July 2022]