Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels’ recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.
Cite this article
Volume | Year
Open Access Original Research
Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques
1 School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, 751024, India
2 School of Computer Application, KIIT University, Bhubaneswar, Odisha, 751024, India
3 Faculty of Health Science, Universiti Sultan Zainal Abidin, Gong Badak Campus, Darul Iman, Terengganu, 21300, Malaysia
4 Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut, Terengganu, 22200, Malaysia
5 Idiap Research Institute, Centre du Parc, Rue Marconi 19, Martigny, CH-1920, Switzerland
*Correspondence: firstname.lastname@example.org (Shantipriya Parida)
J. Integr. Neurosci. 2020, 19(1), 1–9; https://doi.org/10.31083/j.jin.2020.01.24
Submitted: 3 February 2020 | Accepted: 4 March 2020 | Published: 30 March 2020
Copyright: © 2020 Sahu et al. Published by IMR Press.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
artificial neural networks