Neural Computing and Applications

, Volume 31, Issue 1, pp 317–325 | Cite as

Epileptic seizure detection using hybrid machine learning methods

  • Abdulhamit SubasiEmail author
  • Jasmin Kevric
  • M. Abdullah Canbaz
Original Article


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.


Discrete wavelet transform (DWT) Electroencephalogram (EEG) Epileptic seizure Genetic algorithm (GA) Particle swarm optimization (PSO) Support vector machines (SVMs) 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.College of Engineering, Computer Science DepartmentEffat UniversityJeddahSaudi Arabia
  2. 2.Faculty of Engineering and Information Technologies, Department of Electrical and Electronics EngineeringInternational Burch UniversitySarajevoBosnia and Herzegovina
  3. 3.College of Engineering, Department of Computer Science and EngineeringUniversity of Nevada, RenoRenoUSA

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