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Journal of Integrative Neuroscience  2020, Vol. 19 Issue (1): 1-9    DOI: 10.31083/j.jin.2020.01.24
Original Research | Next articles
Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques
Rekha Sahu1, Satya Ranjan Dash2, Lleuvelyn A Cacha3, Roman R Poznanski4, Shantipriya Parida5, *()
1School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, 751024, India
2School of Computer Application, KIIT University, Bhubaneswar, Odisha, 751024, India
3Faculty of Health Science, Universiti Sultan Zainal Abidin, Gong Badak Campus, Darul Iman, Terengganu, 21300, Malaysia
4Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut, Terengganu, 22200, Malaysia
5Idiap Research Institute, Centre du Parc, Rue Marconi 19, Martigny, CH-1920, Switzerland
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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.

Key words:  Epilepsy      seizure      deep learning      artificial neural networks      neural signals      EEG signals      computer simulations     
Submitted:  03 February 2020      Accepted:  04 March 2020      Published:  30 March 2020     
*Corresponding Author(s):  Shantipriya Parida     E-mail:

Cite this article: 

Rekha Sahu, Satya Ranjan Dash, Lleuvelyn A Cacha, Roman R Poznanski, Shantipriya Parida. Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques. Journal of Integrative Neuroscience, 2020, 19(1): 1-9.

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Figure 1.  Overall work on EEG Data set with the implementation of CNN, ensemble, and traditional machine learning algorithms. The EEG dataset is preprocessed (except CNN model) to eliminate irrelevant features and split into train and test datasets. The training and test datasets are used to train the traditional, ensemble, and deep learning models and used to classify epilepsy or non-epilepsy Seizure.

Table 1  Summary of the epileptic EEG data. All Set (A-E) contains five healthy subjects.
Subjects Set A
100 subjects
Set B
100 subjects
Set C
100 subjects
Set D
100 subjects
Set E
100 subjects
Patient’s state Epilepsy seizure Having tumor Healthy Eye closed Eye opened
Number of text files containing recording of EEG signals 100 with each file includes 4096 samples of one EEG time series. 100 with each file includes 4096 samples of one EEG time series. 100 with each file includes 4096 samples of one EEG time series. 100 with each file includes 4096 samples of one EEG time series. 100 with each file includes 4096 samples of one EEG time series.
Time duration (s) 23.6 23.6 23.6 23.6 23.6
Figure 2.  CNN model performance by depicting ROC AUC representation of CNN classifier. The area under the curve is 0.99, which is a valid positive rate.

Table 2  Accuracy and Standard Deviation of different machine learning techniques.
Machine Learning Techniques Accuracy Standard Deviation
Decision tree 0.8886 +/- 0.0014
Random Forest classifier 0.9517 +/- 0.0009
Extra tree classifier 0.9435 +/- 0.0030
Kernel Support Vector Machine (polynomial) 0.9349 +/- 0.0010
Kernel Support Vector Machine (Gaussian) 0.9420 +/- 0.0037
Naïve Bays Classifier 0.9430 +/- 0.0011
Logistic regression 0.8048 +/- 0.0006
K-nearest neighbor classifier 0.9301 +/- 0.0015
Figure 3.  Representation of the ROC curve of traditional machine learning techniques. Random Forest: ROC, AUC = 1.000; Extra Tree: ROC, AUC = 1.000; K-NN: ROC, AUC = 0.997; Logistic Regression: ROC, AUC = 0.538; Decision Tree: ROC, AUC = 0.767.

Table 3  Different Bagging Classifiers' accuracy.
Base Estimators for Bagging Average Manipulation Voting to estimators
Accuracy Standard Deviation Accuracy Standard Deviation
K-nearest neighbors’ classifier 0.9393 +/- 0.0005 0.93 +/- 0.00
Kernel Support Vector Machine (Gaussian) 0.9448 +/- 0.0015 0.94 +/- 0.01
Ridge Classifier 0.8000 +/- 0.0001 0.80 +/- 00
Logistic regression 0.8008 +/- 0.0003 0.80 +/- 00
Decision tree classifier 0.9019 +/- 0.0041 0.89 +/- 00
Naïve Bays Classifier (Gaussian) 0.9427 +/- 0.0017 0.94 +/- 00
Kernel Support Vector Machine (Polynomial) 0.9309 +/- 0.0019 - -
Random Forest Classifier 0.9474 +/- 0.0014 0.95 +/- 0.00
Extra tree classifier 0.966 +/- 0.0007 0.95 +/- 0.00
Figure 4.  ROC AUC representation of bagging classifiers. Bagging Random Forest: ROC, AUC = 0.995; Bagging Extra Tree: ROC, AUC = 0.998; Meta-bagging K-NN: ROC, AUC = 0.994; Meta-bagging Logistic Regression: ROC, AUC = 0.570; Meta-bagging Decision Tree: ROC, AUC = 0.935.

Table 4  Accuracy of Boosting algorithms implementation.
Boosting Methods Accuracy Standard Deviation
Ada Boost 0.93 +/- 0.00
Gradient boosting algorithm 0.95 +/- 0.00
XG Boost Algorithm 0.95 +/- 0.00
Figure 5.  ROC AUC of Boosting classifiers. Ada Boost: ROC, AUC = 0.965; Grad Boost: ROC, AUC = 0.980; XGB Boost: ROC, AUC = 0.981.

Table 5  Stacking implementation accuracy.
Base Estimators for Stacking Voting to estimators
Accuracy Standard Deviation
K-nearest neighbors classifier 0.9301 +/- 0.0015
Logistic regression 0.8048 +/- 0.0006
Decision tree classifier 0.8886 +/- 0.0014
Naïve BaysClassifier (Gaussian) 0.9430 +/- 0.0011
Random forest classifier 0.9470 +/- 0.0029
Extra tree classifier 0.9435 +/- 0.0030
Stack Classifier (second level classifier logistic regression) 0.9510 +/- 0.0009
Figure 6.  Representation ROC, AUC of stacking implementation. Stacking Random Forest: ROC, AUC = 1.000; Stacking Extra Tree: ROC, AUC = 1.000; Stacking K-NN: ROC, AUC = 0.997; Stacking Logistic Regression: ROC, AUC = 0.538; Stacking Decision Tree: ROC, AUC = 0.767; Stacking 2nd level classifier logistic regression: ROC AUC = 1.000.

Table 6  Summary of optimal accuracy of classification for comparative study.
Classifiers Accuracy ROC AUC
CNN 0.96 0.99
Extra Tree Bagging (Average) 0.96 1.00
Gradient Boosting 0.95 0.98
XG Boosting 0.95 0.98
Stacking 0.95 1.00
Random Forest 0.95 1.00
Figure 7.  Performance summary by depicting ROC AUC of all the optimal classifiers (traditional, ensemble, and deep learning). CNN and Bagging Extra Tree outperforms as compared to classifiers based on the conventional machine learning approach.

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