Abstract
In this paper, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and support vector machines (SVMs) for automatic seizure detection in EEG is proposed called GOA-SVM approach. Various parameters were extracted and employed as the features to train the SVM with radial basis function (RBF) kernel function (SVM-RBF) classifiers. GOA was used for selecting the effective feature subset and the optimal settings of SVMs parameters in order to obtain a successful EEG classification. The experimental results confirmed that the proposed GOA-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100% for normal subject data versus epileptic data. Furthermore, the proposed approach has been compared with Particle Swarm Optimization (PSO) with support vector machines (PSO-SVMs) and SVM using RBF kernel function. The computational results reveal that GOA-SVM approach achieved better classification accuracy outperforms both PSO-SVM and typical SVMs.
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References
Guo, L., Rivero, D., Dorado, J., Munteanu, C.R., Pazos, A.: Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst. Appl. 38(8), 10425–10436 (2011)
Hamad, A., Houssein, E.H., Hassanien, A.E., Fahmy, A.A.: A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 108–117. Springer (2017)
Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S., Koh, J.E.: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl. Based Syst. 88, 85–96 (2015)
Hamad, A., Houssein, E.H., Hassanien, A.E., Fahmy, A.A.: Feature extraction of epilepsy EEG using discrete wavelet transform. In: 2016 12th International Computer Engineering Conference (ICENCO), pp. 190–195. IEEE (2016)
Kumar, Y., Dewal, M., Anand, R.: Epileptic seizures detection in EEG using DWT-based apen and artificial neural network. Sign. Image Video Process. 8(7), 1323–1334 (2014)
Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)
Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39(1), 202–209 (2012)
Houssein, E.H., Kilany, M., Hassanien, A.E.: ECG signals classification: a review. Int. J. Intell. Eng. Inform. 5(4), 376–396 (2017)
Houssein, E.H., Kilany, M., Hassanien, A.E., Snasel, V.: A two-stage feature extraction approach for ECG signals. In: International Afro-European Conference for Industrial Advancement, pp. 299–310. Springer (2016)
Tharwat, A., Hassanien, A.E., Elnaghi, B.E.: A BA-based algorithm for parameter optimization of support vector machine. Pattern Recogn. Lett. 93, 13–22 (2017)
Gaspar, P., Carbonell, J., Oliveira, J.L.: On the parameter optimization of support vector machines for binary classification. J. Integr. Bioinform. (JIB) 9(3), 33–43 (2012)
Department of Epileptology, University of Bonn: EEG time series data. http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html. Accessed Oct 2016
Faust, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26, 56–64 (2015)
Hassanien, A.E., Emary, E.: Swarm Intelligence: Principles, Advances, and Applications. CRC Press, New York (2016)
Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Andrew, A.M.: An introduction to support vector machines and other kernel-based learning methods. Robotica 18(6), 687–689 (2000)
Sharma, R., Pachori, R.B., Gautam, S.: Empirical mode decomposition based classification of focal and non-focal seizure EEG signals. In: 2014 International Conference on Medical Biometrics, pp. 135–140. IEEE (2014)
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Hamad, A., Houssein, E.H., Hassanien, A.E., Fahmy, A.A. (2018). Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_9
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DOI: https://doi.org/10.1007/978-3-319-74690-6_9
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