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

[ Vol. 20 , Issue. 9 ]


Arshpreet Kaur*, Karan Verma, Amol P. Bhondekar and Kumar Shashvat   Pages 755 - 765 ( 11 )


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.


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


National Institute of Technology, New Delhi, National Institute of Technology, New Delhi, Central Scientific Instruments Organization, Chandigarh, National Institute of Technology, New Delhi

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