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

[ Vol. 20 , Issue. 9 ]

Author(s):

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

Abstract:


Background and Objective: 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 is to find how the ensemble model distinguishes between two different set of problem which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter.

Method: The work is divided to conquer two problem groups the first being classifying inter ictal and ictal for which case 1(C-E), case 2(D-E) are considered which is Group 1. The other group (Group 2) is classifying between inter-ictal and controlled for which case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) are considered. To divide the EEG into sub-bands DWT (Discrete Wavelet Transform) is used and approximate Entropy is extracted out of all the five sub-bands of EEG for each case. Bagged SVM is used to classify between different groups considered.

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

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

Keywords:

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

Affiliation:

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



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