Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier
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Date
2023-08
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Publisher
Istanbul University Cerrahpasa
Abstract
Epilepsy is a disease with various forms. However, limited dataset has confined classification studies of epilepsy into binary classes only. This study sort to achieve multiclassification
of epileptic seizures through a robust feature extraction technique by comprehensively analyzing various advanced feature parameters from different
domains, such as energy and entropy. The values of these parameters were computed from the coefficients of dilation wavelet transform (DWT) and modified DWT,
known as dual-tree complex wavelet transform decomposition. The model was evaluated from the features of each of the parameters. The hybrid features were divided
into three experiments to extract the meaningful features as follows: 1). features from combined energy features were extracted; 2). features from combined entropy
features were also extracted; and 3). features from combined parameters as hybrid features were extracted. Finally, the model was developed based on the extracted
features to perform a multi-classification of seven types of seizures using an optimized support vector machine (SVM) classifier. A recently released temple university
hospital corpus dataset consisting of long-time seizure recordings of various seizures was employed to evaluate our proposed model. The proposed optimized SVM
classifier with the hybrid features performed better than other experimented models with the value of accuracy, sensitivity, specificity, precision, and F1-score of 96.9%,
96.8%, 93.4%, 95.6%, and 96.2%, respectively. The developed model was also compared with some recent works in literature that employed the same dataset and found
that our model outperformed all the compared studies.
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Keywords
Multi-classification, EEG, epileptic seizures, DTCWT, hybrid features, SVM