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

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Title Statement <p>Performance Exploration of Multiple Classifiers with Grid Search Hyperparameter Tuning for Detecting Epileptic Seizures from EEG Signals</p>
 
Added Entry - Uncontrolled Name Ganesh Babu, C ; Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu 638 401, India
Gowri Shankar, M ; Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu 638 401, India
Harikumar, R ; Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu 638 401, India
Nil
 
Uncontrolled Index Term Epilepsy, GMM, Grid search, HMM, Hyperparameters
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2022-08-02 07:10:34
 
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http://op.niscair.res.in/index.php/JSIR/article/view/53998
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 81, ##issue.no## 07 (2022): Journal of Scientific and Industrial Research
 
Language Note en