JACIII Vol.19 No.3 pp. 447-455
doi: 10.20965/jaciii.2015.p0447


A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification

Khaled A. Abuhasel*, Abdullah M. Iliyasu*,**, and Chastine Fatichah***

*College of Engineering, Salman Bin Abdulaziz University
Al-Kharj, Kingdom of Saudi Arabia

**Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology
G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

***Informatics Department, Institut Teknologi Sepuluh Nopember
ITS Campus, Sukolilo, Surabaya 60111, Surabaya, East Java, Indonesia

October 31, 2014
March 11, 2015
May 20, 2015
epileptic seizure detection, fuzzy membership, neural network, particle swarm optimization, EEG signal
A hybrid particle swarm optimization (PSO) integrating neural network with fuzzy membership function (NEWFM) technique is proposed for epileptic seizure classification tasks based on brain electroencephalography (EEG) signals. By combining PSO and NEWFM, the proposed method obtains the optimal parameters from the EEG data training required to achieve the best accuracy in disease diagnosis. NEWFM, a model of neural networks, is expected to improve the accuracy by updating weights of fuzzy membership functions. The PSO, a swarm-inspired optimization algorithm, is used to obtain the optimal parameters from the NEWFM. A standard dataset comprising of 5 sets of epileptic seizure detection data, each consisting 100 single EEGs segments is employed to evaluate the proposed technique’s performance. Based on the experiments, the classification results show that the best accuracy of Z–S classification task is 99.5% with the optimal parameters of α = 0.1 and β=0.1. For the ZNF–S classification task, the best accuracy is 97.73% with the optimal parameters of α=0.1 or 0.2 and β =0.2. Similar results for the ZNFO–S classification task is 97.64% with the optimal parameters set at α =0.1 or 0.2 and β = 0.1.
Cite this article as:
K. Abuhasel, A. Iliyasu, and C. Fatichah, “A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 447-455, 2015.
Data files:
  1. [1] N. Fisher, S. Talathi, A. Cadotte, and P. R. Carney, “Epilepsy detection and monitoring,” Quantitative EEG Analysis Methods and Clinical Applications, Artech House Publishers, pp. 157-183, 2008.
  2. [2] J. P. Betancourt, C. Fatichah, M. L. Tangel, F. Yan, A. Garcia, F. Dong, and K. Hirota, “Similarity-based fuzzy classification of ECG and capnogram signals,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 302-310, 2013.
  3. [3] R. HariKumar and T. Vijayakumar, “Performance analysis of patient specific elman-chaotic optimization model for fuzzy based epilepsy risk level classification from EEG signals,” Int. J. on Smart Sensing And Intelligent Systems, Vol.2, No.4, 2009.
  4. [4] U. Orhan, M. Hekim, M. Ozer, and I. Provaznik, “Epilepsy diagnosis using probability density functions of EEG signals,” Innovations in Intelligent Systems and Applications (INISTA), Istanbul, Turkey, pp. 626-630, 15-18 June, 2011.
  5. [5] L. Guo, D. Rivero, J. Dorado, J. R. Rabu nal, and A. Pazos, “Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks,” J. of Neuroscience Methods, Vol.191, pp. 101-109, 2010.
  6. [6] H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Trans. On Biomedical Engineering, Vol.54, No.2, pp. 205-211, 2007.
  7. [7] V. A. Golovko, S. V. Bezobrazova, S. V. Bezobrazov, and U. S. Rubanau, “Application of neural networks to the electroencephalogram analysis for epilepsy detection,” Proc. of Int. Joint Conf.e on Neural Networks, Orlando, Florida, USA, August 12-17, 2007.
  8. [8] M. Han and L. Sun, “EEG signal classification for epilepsy diagnosis based on AR model and RVM,” Proc. of Int. Conf. on Intelligent Control and Information Processing, Dalian, China, August 13-15, 2010.
  9. [9] S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, “Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection,” IEEE Trans. on Biomedical Engineering, Vol.55, No.2, pp. 512-518, 2008.
  10. [10] V. Srinivasan, C. Eswaran, and N. Sriraam, “Artificial neural network based epileptic detection using time-domain and frequency-domain features,” J. of Medical Systems, Vol.29, No.2, pp. 647-60, 2005.
  11. [11] H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Systems with Applications, Vol.36, No.2, pp. 2027-2036, 2009.
  12. [12] V. Nigam and D. Graupe, “A neural-network-based detection of epilepsy,” Neurological Research, Vol.26, No.1, pp. 55-60, 2004.
  13. [13] C. Fatichah, A. M. Iliyasu, K. A. Abuhasel, N. Suciati, and M. A. Al-Qodah, “Principal Component Analysis-based Neural Network with Fuzzy Membership Function For Epileptic Seizure Detection,” 2014 10th Int. Conf. on Natural Computation (ICNC), Xiamen, China, 19-21 August, pp. 186-191, 2014.
  14. [14] J. S. Lim, T. W. Ryu, H. J. Kim, and S. Gupta, “Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions,” LNAI 3614, Springer, pp. 811-820, 2005.
  15. [15] J. S. Lim, “Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system,” IEEE Trans. on Neural Networks, Vol.20, No.3, pp. 522-527, 2009.
  16. [16] S. H Lee and J. S. Lim, “Forecasting KOSPI based on a neural network with weighted fuzzy membership functions,” Expert Systems with Applications, Vol.38, pp. 4259-4263, 2011.
  17. [17] M. Singh and S. Kaur, “Epilepsy detection using EEG an overview,” Int. J. of Information Technology and Knowledge Management December, Vol.6, No.1, pp. 3-5, 2012.
  18. [18] L. Shi, R. Duan, and B. Lu, “A robust principal component analysis algorithm for EEG-based vigilance estimation,” Engineering in Medicine and Biology Society (EMBC), 35th Annual Int. Conf. of the IEEE, Osaka, Japan, 3-7 July 2013, pp. 6623-6626, 2013.
  19. [19] N. Kwak, “Principal component analysis based on L1-norm maximization,” IEEE Tran. Pattern Analysis and Machine Intelligence, Vol.30, No.9, pp. 1672-1680, 2008.
  20. [20] H. Lee and S. Choi, “PCA+HMM+SVM for EEG pattern classification,” Proc. 7th Int. Symp. Signal Process. and Its Applicat., Vol.1, pp. 541-544, 2003.
  21. [21] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” The IEEE Proc. of Int. Conf. on Neural Networks, pp. 1942-1948, 1995.
  22. [22] C. Grosan, A. Abraham, and M. Chis, “Swarm Intelligence in Data Mining,” Studies in Computational Intelligence (SCI), Vol.34, Springer-Verlag, pp. 1-20, 2006.
  23. [23] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state,” Physical Review,Vol.E64, No.2, 061907, pp. 1-8, 2001.
  24. [24] K. A. Abuhasel, A. M. Iliyasu, and C. Fatichah, “A Combined AdaBoost and NEWFM Technique for Medical Data Classification,” Springer Information Science and Applications, Vol.339, pp. 801-809, doi: 10.1007/978-3-662-46578-3_95, 2015.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024