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- Title
Comparison of Classification Models Using Entropy Based Features from Sub-bands of EEG.
- Authors
Kaur, Arshpreet; Verma, Karan; Bhondekar, Amol P.; Shashvat, Kumar
- Abstract
The purpose of this study is to distinguish between different epileptic states automatically in an EEG. The work focuses on distinguishing activity of a controlled patient from interictal and ictal activity and also from each other. Publically available Bonn database is used in this study. Seven such cases are considered. For this study three entropy features: approximate entropy, sample entropy and fuzzy approximate entropy are extracted from frequency sub-bands and are used with six classification algorithms which are Naive Bayes, LDA (Linear Discriminant Analysis), QDA(Quadratic Discriminant Analysis) from the generative group and RF(Random Forest), GB(Gradient Boosting) and Ada Boost from the ensemble group. The performance is evaluated on basis ofClassification accuracy, Sensitivity and Specificity.The results obtained direct that LDA as a classifier from the generative class and Ada boost from the ensemble group has outperformed other classifiers achieving the highest classification accuracies for three cases each respectively. Evaluating the results from sub-bands, we find out that D2 (21.7-43.4 Hz) sub-band clearly outperformed all the bands. Among the entropies used as features from sub bands, sample entropy outperforms the other entropies. From the results obtained it is established that frequency features from higher sub-band such as D2 (21.7-43.4 Hz) contain substantial information which can be used for identification of epileptic discharges which are however missed during visual analysis. This shows the impact automated methods can make in the field of identification of ictal and inter-ictal activity.
- Publication
Traitement du Signal, 2020, Vol 37, Issue 2, p279
- ISSN
0765-0019
- Publication type
Academic Journal
- DOI
10.18280/ts.370214