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Epileptic seizure detection using hybrid machine learning methods

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Abstract

The aim of this study is to establish a hybrid model for epileptic seizure detection with genetic algorithm (GA) and particle swarm optimization (PSO) to determine the optimum parameters of support vector machines (SVMs) for classification of EEG data. SVMs are one of the robust machine learning techniques and have been extensively used in many application areas. The kernel parameter’s setting for SVMs in training process effects the classification accuracy. We used GA- and PSO-based approach to optimize the SVM parameters. Compared to the GA algorithm, the PSO-based approach significantly improves the classification accuracy. It is shown that the proposed Hybrid SVM can reach a classification accuracy of up to 99.38% for the EEG datasets. Hence, the proposed Hybrid SVM is an efficient tool for neuroscientists to detect epileptic seizure in EEG.

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Notes

  1. EEG time series are available under (http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html).

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Authors and Affiliations

  1. College of Engineering, Computer Science Department, Effat University, Jeddah, 21478, Saudi Arabia

    Abdulhamit Subasi

  2. Faculty of Engineering and Information Technologies, Department of Electrical and Electronics Engineering, International Burch University, Francuske Revolucije bb. Ilidza, 71000, Sarajevo, Bosnia and Herzegovina

    Jasmin Kevric

  3. College of Engineering, Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, 89557, USA

    M. Abdullah Canbaz

Authors
  1. Abdulhamit Subasi
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  2. Jasmin Kevric
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  3. M. Abdullah Canbaz
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Correspondence to Abdulhamit Subasi.

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Subasi, A., Kevric, J. & Abdullah Canbaz, M. Epileptic seizure detection using hybrid machine learning methods. Neural Comput & Applic 31, 317–325 (2019). https://doi.org/10.1007/s00521-017-3003-y

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  • DOI: https://doi.org/10.1007/s00521-017-3003-y

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