Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.
DOI:
https://doi.org/10.29304/jqcm.2023.15.3.1266Keywords:
Feature selection, EEG data, machine learning algorithms, classification, wrapper method, Filter methodAbstract
This research presents a survey for analyzing and classifying the EEG signal based on feature selection approaches. Moreover, The increasing complexity of high-dimensional medical datasets necessitates efficient feature selection methods for early disease detection and safeguarding public health. Intelligent feature selection represents an advanced stage in machine learning and innovative computer applications, as it reduces the number of features required for accurate classification. Generally, The main goal of feature selection is to improve the predictive model's performance and reduce the computational cost of modeling. This paper contains a survey of considerable research several on feature selection. The main measures to analyze this paper are Accuracy, precision, Recall, and F1-score assessment. In order to evaluate performance used stander dataset are EEG Bonn University. The results have proven that they have achieved the highest accuracy rate of around 99% compared with different techniques.
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References
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[21] V. S. Hemachandira and R. Viswanathan, “A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection,” J Healthc Eng, vol. 2022, 2022, doi: 10.1155/2022/7654666.
[22] S. Wang and Y. Jiang, “Exploration of Smart Medical Technology Based on Intelligent Computing Methods,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, 2021, pp. 284–293. doi: 10.1007/978-3-030-84529-2_24.
[23] M. Panigrahi, D. K. Behera, and K. C. Patra, “A Hybrid Model for Epileptic Seizure Classification Using Wavelet Packet Decomposition and SVM,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2021, pp. 211–219. doi: 10.1007/978-981-16-0695-3_21.
[24] L. A. Moctezuma and M. Molinas, “EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization,” Front Neurosci, vol. 14, Jun. 2020, doi: 10.3389/fnins.2020.00593.
[2] A. Malekzadeh, A. Zare, M. Yaghoobi, and R. Alizadehsani, “Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method,” Big Data and Cognitive Computing, vol. 5, no. 4, Dec. 2021, doi: 10.3390/bdcc5040078.
[3] M. R. Aziz and A. S. Alfoudi, “Feature Selection of The Anomaly Network Intrusion Detection Based on Restoration Particle Swarm Optimization,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 5, pp. 592–600, Oct. 2022, doi: 10.22266/ijies2022.1031.51.
[4] S. T. George, M. S. P. Subathra, N. J. Sairamya, L. Susmitha, and M. Joel Premkumar, “Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform,” Biocybern Biomed Eng, vol. 40, no. 2, pp. 709–728, Apr. 2020, doi: 10.1016/j.bbe.2020.02.001.
[5] A. H. Alsaeedi, A. L. Albukhnefis, D. Al-Shammary, and I. M. Al-Asfoor, “Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data.”
[6] W. Sun, Y. Su, X. Wu, X. Wu, and Y. Zhang, “EEG denoising through a wide and deep echo state network optimized by UPSO algorithm,” Appl Soft Comput, vol. 105, Jul. 2021, doi: 10.1016/j.asoc.2021.107149.
[7] Z. Zhu, Y. S. Ong, and M. Dash, “Wrapper-filter feature selection algorithm using a memetic framework,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 1, pp. 70–76, Feb. 2007, doi: 10.1109/TSMCB.2006.883267.
[8] S. Maldonado and R. Weber, “A wrapper method for feature selection using Support Vector Machines,” Inf Sci (N Y), vol. 179, no. 13, pp. 2208–2217, Jun. 2009, doi: 10.1016/j.ins.2009.02.014.
[9] E. Hancer, B. Xue, and M. Zhang, “Differential evolution for filter feature selection based on information theory and feature ranking,” Knowl Based Syst, vol. 140, pp. 103–119, Jan. 2018, doi: 10.1016/j.knosys.2017.10.028.
[10] Z. Lan, Y. Liu, O. Sourina, L. Wang, R. Scherer, and G. Müller-Putz, “SAFE: An EEG dataset for stable affective feature selection,” Advanced Engineering Informatics, vol. 44, Apr. 2020, doi: 10.1016/j.aei.2020.101047.
[11] M. Radman, M. Moradi, A. Chaibakhsh, M. Kordestani, and M. Saif, “Multi-Feature Fusion Approach for Epileptic Seizure Detection from EEG Signals,” IEEE Sens J, vol. 21, no. 3, pp. 3533–3543, Feb. 2021, doi: 10.1109/JSEN.2020.3026032.
[12] A. P. Gini and M. P. F. Queen, “An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier,” Webology, vol. 18, no. Special Issue, pp. 327–340, 2021, doi: 10.14704/WEB/V18SI04/WEB18132.
[13] Z. Brari and S. Belghith, “A new Machine Learning approach for epilepsy diagnostic based on Sample Entropy,” in IFAC-PapersOnLine, Elsevier B.V., 2021, pp. 346–351. doi: 10.1016/j.ifacol.2021.10.280.
[14] G. Singh, M. Kaur, and B. Singh, “Detection of Epileptic Seizure EEG Signal Using Multiscale Entropies and Complete Ensemble Empirical Mode Decomposition,” Wirel Pers Commun, vol. 116, no. 1, pp. 845–864, Jan. 2021, doi: 10.1007/s11277-020-07742-z.
[15] M. Açıkoğlu and S. A. Tuncer, “Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis,” Med Hypotheses, vol. 135, Feb. 2020, doi: 10.1016/j.mehy.2019.109464.
[16] V. K. Mehla, A. Singhal, and P. Singh, “An Efficient Classification of Focal and Non-Focal EEG Signals Using Adaptive DCT Filter Bank,” Circuits Syst Signal Process, Aug. 2023, doi: 10.1007/s00034-023-02328-z.
[17] Aayesha, M. B. Qureshi, M. Afzaal, M. S. Qureshi, and M. Fayaz, “Machine learning-based EEG signals classification model for epileptic seizure detection,” Multimed Tools Appl, vol. 80, no. 12, pp. 17849–17877, May 2021, doi: 10.1007/s11042-021-10597-6.
[18] Y. Jiang, W. Chen, M. Li, T. Zhang, and Y. You, “Synchroextracting chirplet transform-based epileptic seizures detection using EEG,” Biomed Signal Process Control, vol. 68, Jul. 2021, doi: 10.1016/j.bspc.2021.102699.
[19] S. Ibrahim, R. Djemal, and A. Alsuwailem, “Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis,” Biocybern Biomed Eng, vol. 38, no. 1, pp. 16–26, 2018, doi: 10.1016/j.bbe.2017.08.006.
[20] S. L. Zhang, B. Zhang, Y. L. Su, and J. L. Song, “A novel EEG-complexity-based feature and its application on the epileptic seizure detection,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 12, pp. 3339–3348, Dec. 2019, doi: 10.1007/s13042-019-00921-w.
[21] V. S. Hemachandira and R. Viswanathan, “A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection,” J Healthc Eng, vol. 2022, 2022, doi: 10.1155/2022/7654666.
[22] S. Wang and Y. Jiang, “Exploration of Smart Medical Technology Based on Intelligent Computing Methods,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, 2021, pp. 284–293. doi: 10.1007/978-3-030-84529-2_24.
[23] M. Panigrahi, D. K. Behera, and K. C. Patra, “A Hybrid Model for Epileptic Seizure Classification Using Wavelet Packet Decomposition and SVM,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2021, pp. 211–219. doi: 10.1007/978-981-16-0695-3_21.
[24] L. A. Moctezuma and M. Molinas, “EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization,” Front Neurosci, vol. 14, Jun. 2020, doi: 10.3389/fnins.2020.00593.
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Published
2023-09-30
How to Cite
Hakem, E., Al-Shammary, D., & Mahdi, A. M. (2023). Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(3), Comp Page 71–82. https://doi.org/10.29304/jqcm.2023.15.3.1266
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Computer Articles
