Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Year 2019, , 27 - 37, 30.06.2019

Sema Yildirim , Hasan Erdinç Koçer , A.hakan Ekmekci

https://doi.org/10.18100/ijamec.475090

Abstract

Artificial
Neural Networks (ANNs) that are the ability to learn from theirs environment in
order to improve their performance are widely used in numerous applications. The
Backpropagation (BP) Algorithm is one of the most popular and effective model
of ANNs. However, since it uses gradient descent algorithm which attempts to
minimize the error of the network by moving gradient of the error curve, easily
get trapped at local minima. To avoid this problem, we proposed an ANNs and Swarm
Intelligence (SI) method, where Artificial Bee Colony (ABC) and Particle Swarm
Optimization (PSO) algorithms were operated for the Multilayer Perceptron
Neural Network (MLPNN) weights update. Two Electroencephalogram (EEG) datasets
were used to test the success of all algorithms including ABC-MLPNN, PSO-MLPNN and
conventional-MLPNN. Compared to conventional-MLPNN, higher success values were
obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN
algorithm has been increased the success of BP algorithm by avoiding local
minima. 

Keywords

Swarm intelligence, artificial neural networks, backpropagation, electroencephalogram, brain disorders, feature extraction/selection

References

• Reference 1. W. G. Baxt, “Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion,” Neural Comput., vol. 2, no. 4, pp. 480–489, 1990.
• Reference 2. I. Gule, E. D. Ubeyli, and N. F. Guler, “A mixture of experts network structure for EEG signals classification,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, 2006, pp. 2707–2710.
• Reference 3. E. D. Übeyli, “Lyapunov exponents/probabilistic neural networks for analysis of EEG signals,” Expert Syst. Appl., vol. 37, no. 2, pp. 985–992, 2010.
• Reference 4. G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, 2014.
• Reference 5. I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., vol. 3, no. 3, pp. 1157–1182, 2003.
• Reference 6. E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Principles of neural science, vol. 4. McGraw-Hill New York, 2000.
• Reference 7. H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of EEG records in an epileptic patient using wavelet transform,” J. Neurosci. Methods, vol. 123, no. 1, pp. 69–87, 2003.
• Reference 8. L. D. Iasemidis et al., “Adaptive epileptic seizure prediction system,” Biomed. Eng. IEEE Trans., vol. 50, no. 5, pp. 616–627, 2003.
• Reference 9. W. O. Tatum IV, “Long-term EEG monitoring: a clinical approach to electrophysiology,” J. Clin. Neurophysiol., vol. 18, no. 5, pp. 442–455, 2001.
• Reference 10. L. Guo, D. Rivero, and A. Pazos, “Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks,” J. Neurosci. Methods, vol. 193, no. 1, pp. 156–163, 2010.
• Reference 11. K. Fu, J. Qu, Y. Chai, and Y. Dong, “Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM,” Biomed. Signal Process. Control, vol. 13, pp. 15–22, Sep. 2014.
• Reference 12. V. Joshi, R. B. Pachori, and A. Vijesh, “Classification of ictal and seizure-free EEG signals using fractional linear prediction,” Biomed. Signal Process. Control, vol. 9, pp. 1–5, Jan. 2014.
• Reference 13. T. Gandhi, B. K. Panigrahi, and S. Anand, “A comparative study of wavelet families for EEG signal classification,” Neurocomputing, vol. 74, no. 17, pp. 3051–3057, Oct. 2011.
• Reference 14. S.-H. Lee, J. S. Lim, J.-K. Kim, J. Yang, and Y. Lee, “Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance,” Comput. Methods Programs Biomed., vol. 116, no. 1, pp. 10–25, 2014.
• Reference 15. S. Aydın, H. M. Saraoğlu, and S. Kara, “Log energy entropy-based EEG classification with multilayer neural networks in seizure,” Ann. Biomed. Eng., vol. 37, no. 12, pp. 2626–2630, 2009.
• Reference 16. K. A. Abuhasel, A. M. 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. Informatics, vol. 19, no. 3, pp. 447–455, 2015.
• Reference 17. S. K. Satapathy, S. Dehuri, and A. K. Jagadev, “ABC optimized RBF network for classification of EEG signal for epileptic seizure identification,” Egypt. Informatics J., 2016.
• Reference 18. S. Dehuri, S. Ghosh, and S.-B. Cho, Integration of swarm intelligence and artificial neural network. World Scientific, 2011.
• Reference 19. A. H. Shoeb, “Application of machine learning to epileptic seizure onset detection and treatment.” Massachusetts Institute of Technology, 2009.
• Reference 20. I. Güler and E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” J. Neurosci. Methods, vol. 148, no. 2, pp. 113–121, 2005.
• Reference 21. P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press, 2017.
• Reference 22. I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans. Inf. theory, vol. 36, no. 5, pp. 961–1005, 1990.
• Reference 23. S. Soltani, “On the use of the wavelet decomposition for time series prediction,” Neurocomputing, vol. 48, no. 1, pp. 267–277, 2002.
• Reference 24. M. Akay, “Wavelet applications in medicine,” IEEE Spectr., vol. 34, no. 5, pp. 50–56, 1997.
• Reference 25. M. Unser and A. Aldroubi, “A review of wavelets in biomedical applications,” Proc. IEEE, vol. 84, no. 4, pp. 626–638, 1996.
• Reference 26. M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi, “Wavelet toolbox,” MathWorks Inc., Natick, MA, vol. 15, p. 21, 1996.
• Reference 27. K. N. Le, K. P. Dabke, and G. K. Egan, “Hyperbolic wavelet power spectra of nonstationary signals,” Opt. Eng., vol. 42, no. 10, pp. 3017–3037, 2003.
• Reference 28. S. Prabhakar, A. R. Mohanty, and A. S. Sekhar, “Application of discrete wavelet transform for detection of ball bearing race faults,” Tribol. Int., vol. 35, no. 12, pp. 793–800, 2002.
• Reference 29. A. Moreno-Muñoz, Power quality: mitigation technologies in a distributed environment. Springer Science & Business Media, 2007.
• Reference 30. Y. Li, P. P. Wen, Siuly, Y. Li, and P. P. Wen, “Clustering technique-based least square support vector machine for EEG signal classification,” Comput. Methods Programs Biomed., vol. 104, no. 3, pp. 358–372, Dec. 2011.
• Reference 31. S. Lakhina, S. Joseph, and B. Verma, “Feature reduction using principal component analysis for effective anomaly–based intrusion detection on NSL-KDD,” 2010.
• Reference 32. U. Rajendra Acharya et al., “Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework,” Expert Syst. Appl., vol. 39, no. 10, pp. 9072–9078, Aug. 2012.
• Reference 33. I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods, vol. 43, no. 1, pp. 3–31, 2000.
• Reference 34. U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., vol. 38, no. 10, pp. 13475–13481, 2011.
• Reference 35. L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, and A. Pazos, “Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks,” J. Neurosci. Methods, vol. 191, no. 1, pp. 101–109, 2010.
• Reference 36. S. Das and A. Konar, “A swarm intelligence approach to the synthesis of two-dimensional IIR filters,” Eng. Appl. Artif. Intell., vol. 20, no. 8, pp. 1086–1096, 2007.
• Reference 37. F. S. Abu-Mouti and M. E. El-Hawary, “Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm,” IEEE Trans. power Deliv., vol. 26, no. 4, pp. 2090–2101, 2011.
• Reference 38. Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69–73.
• Reference 39. J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia weight strategies in particle swarm optimization,” in Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on, 2011, pp. 633–640.
• Reference 40. P. C. Chen and Y. K. Hwang, “SANDROS: a dynamic graph search algorithm for motion planning,” Robot. Autom. IEEE Trans., vol. 14, no. 3, pp. 390–403, 1998.
• Reference 41. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the sixth international symposium on micro machine and human science, 1995, vol. 1, pp. 39–43.
• Reference 42. G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM Sigkdd Explor. Newsl., vol. 6, no. 1, pp. 20–29, 2004.
• Reference 43. T. R. Patil and S. S. Sherekar, “Performance analysis of Naive Bayes and J48 classification algorithm for data classification,” Int. J. Comput. Sci. Appl., vol. 6, no. 2, pp. 256–261, 2013.
• Reference 44. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006.
• Reference 45. U. R. Acharya, S. V. Sree, A. P. C. Alvin, and J. S. Suri, “Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework,” Expert Syst. Appl., vol. 39, no. 10, pp. 9072–9078, 2012.
• Reference 46. Y. Kaya, M. Uyar, R. Tekin, and S. Yıldırım, “1D-local binary pattern based feature extraction for classification of epileptic EEG signals,” Appl. Math. Comput., vol. 243, pp. 209–219, 2014.
• Reference 47. D. Wang, D. Miao, and C. Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection,” Expert Syst. Appl., vol. 38, no. 11, pp. 14314–14320, 2011.
• Reference 48. N. Hazarika, J. Z. Chen, A. C. Tsoi, and A. Sergejew, “Classification of EEG signals using the wavelet transform,” in Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on, 1997, vol. 1, pp. 89–92.
• Reference 49. A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Syst. Appl., vol. 32, no. 4, pp. 1084–1093, May 2007.
• Reference 50. E. D. Übeyli, “Statistics over features: EEG signals analysis,” Comput. Biol. Med., vol. 39, no. 8, pp. 733–741, 2009.
• Reference 51. P. Berg and M. Scherg, “A multiple source approach to the correction of eye artifacts,” Electroencephalogr. Clin. Neurophysiol., vol. 90, no. 3, pp. 229–241, 1994.
• Reference 52. M. Y. Rafiq, G. Bugmann, and D. J. Easterbrook, “Neural network design for engineering applications,” Comput. Struct., vol. 79, no. 17, pp. 1541–1552, 2001.
• Reference 53. Q. Gui, Z. Jin, and W. Xu, “Exploring EEG-based biometrics for user identification and authentication,” 2014 IEEE Signal Process. Med. Biol. Symp. IEEE SPMB 2014 - Proc., 2015.