Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG

Author(s): Arshpreet Kaur*, Karan Verma, Amol P. Bhondekar, Kumar Shashvat.

Journal Name: Current Pharmaceutical Biotechnology

Volume 20 , Issue 9 , 2019

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Graphical Abstract:


Background: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter.

Methods: This work addresses the classification problem for two groups; Group 1: “inter-ictal vs. ictal” for which case 1(C-E), and case 2(D-E) are included and Group 2; “activity from controlled vs. inter-ictal activity” considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered.

Results: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data.

Conclusion: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

Keywords: EEG classification, approximate entropy, discrete wavelet transform, bagged SVM, ensemble model, epileptic states.

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Year: 2019
Page: [755 - 765]
Pages: 11
DOI: 10.2174/1389201020666190618112715
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