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.