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Article

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means

by
Khaled M. Alalayah
1,*,
Ebrahim Mohammed Senan
2,*,
Hany F. Atlam
3,
Ibrahim Abdulrab Ahmed
4 and
Hamzeh Salameh Ahmad Shatnawi
4
1
Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
2
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a P.O. Box 1152, Yemen
3
Cyber Security Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
4
Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Diagnostics 2023, 13(11), 1957; https://doi.org/10.3390/diagnostics13111957
Submission received: 1 April 2023 / Revised: 22 May 2023 / Accepted: 2 June 2023 / Published: 3 June 2023
(This article belongs to the Special Issue Artificial Intelligence in Neuroimaging for Diagnosis)

Abstract

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.
Keywords: EEG; epileptic seizure; DWT; K-means; PCA; t-SNE; machine learning EEG; epileptic seizure; DWT; K-means; PCA; t-SNE; machine learning

Share and Cite

MDPI and ACS Style

Alalayah, K.M.; Senan, E.M.; Atlam, H.F.; Ahmed, I.A.; Shatnawi, H.S.A. Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics 2023, 13, 1957. https://doi.org/10.3390/diagnostics13111957

AMA Style

Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics. 2023; 13(11):1957. https://doi.org/10.3390/diagnostics13111957

Chicago/Turabian Style

Alalayah, Khaled M., Ebrahim Mohammed Senan, Hany F. Atlam, Ibrahim Abdulrab Ahmed, and Hamzeh Salameh Ahmad Shatnawi. 2023. "Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means" Diagnostics 13, no. 11: 1957. https://doi.org/10.3390/diagnostics13111957

APA Style

Alalayah, K. M., Senan, E. M., Atlam, H. F., Ahmed, I. A., & Shatnawi, H. S. A. (2023). Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics, 13(11), 1957. https://doi.org/10.3390/diagnostics13111957

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