An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT

- Journal title : Journal of Electrical Engineering and Technology
- Volume 9, Issue 3, 2014, pp.1060-1071
- Publisher : The Korean Institute of Electrical Engineers
- DOI : 10.5370/JEET.2014.9.3.1060

Title & Authors

An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT

Sivasankari, K.; Thanushkodi, K.;

Sivasankari, K.; Thanushkodi, K.;

Abstract

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

Keywords

Adaptive neuro fuzzy inference system (ANFIS);Backpropagation neural network (BPNN);EEG signal;Epileptic seizure;Independent component analysis (ICA);Levenberg-marquardt algorithm;Neural network classification;Short time fourier transform (STFT);and thresholding;

Language

English

Cited by

1.

References

1.

Frederic, Alexandre, KerkeniNizar, Ben Khalifa Khaled, Bedoui Mohamed Hedi, Bougrain Laurent, and Dogui Mohamed, "Supervised Neuronal Approaches for EEG Signal Classification: Experimental Studies," Artificial Intelligence and Soft Computing. ACTA Press, vol. 29, 2006, pp. 1-6.

2.

George Tsolis and Thomas D. Xenos, "Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 4, no. 2, June 2011, pp. 91- 106.

3.

NihalFatma Guler, ElifDerya Ubeyli and Inan Guler, "Recurrent Neural Networks Employing Lyapunov Exponents for EEG Signals Classification," Expert Systems with Applications, vol. 29, no. 3, 2005, pp. 506- 514.

4.

Claude Robert, Jean-Francois Gaudy, Aime Limoge, "Electroencephalogram Processing Using Neural Networks," Clinical Neurophysiology, vol. 113, no. 5, 2002, pp. 694- 701.

5.

James, Christopher J., and Christian W. Hesse. "Independent Component Analysis for Biomedical Signals," Physiological measurement, vol. 26, no. 1, 2004, pp. 633-644.

6.

ShaikJakeer Husain 1 and K.Srinivasa Rao, "Epileptic Seizures Classification from EEG Signals using Neural Networks," International Conference on Information and Network Technology, vol. 37, 2012, pp. 269- 273.

7.

Mahbubul Alam, Md. Imdadul Islam, and M. R. Amin, "Performance Comparison of STFT, WT, LMS and RLS Adaptive Algorithms in Denoising of Speech Signal," IACSIT International Journal of Engineering and Technology, vol.3, no.3, June 2011, pp. 235- 238.

8.

Hamid R. Mohseni, A. Maghsoudi and Mohammad B. Shamsollahi, "Seizure Detection in EEG signals: A Comparison of Different Approaches," In Conf Proc IEEE Eng Med BiolSoc, 2006, pp. 6724-6727.

9.

Zeynep Yucel and A. Bulent Ozguler, "Detection of Epileptic Indicators on Clinical Sub-bands of EEG," 16th European Signal Processing Conference, 2008, pp. 1-5.

10.

Inan Guler, Eli fDerya Ubeyli and Nihal Fatma Guler, "A Mixture of Experts Network Structure for EEG Signals Classification," Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005, pp. 2707- 2710.

11.

Elif Derya Ubeyli, "Analysis of EEG Signals by Implementing Eigenvector Methods/Recurrent Neural Networks," Digital Signal Processing, vol. 19, no. 1, 2009, pp.134-143.

12.

Sergiy Vorobyov and Andrzej Cichocki, "Blind Noise Reduction for Multisensory Signals using ICA and Subspace Filtering, with Application to EEG Analysis," Biol. Cybern., vol. 86, no. 4, 2002, pp. 293-303.

13.

Arun S. Chavan and Mahesh Kolte, "EEG Signal Preprocessing using Wavelet Transform," International Journal of Electronics Engineering, vol. 3, no. 1, 2011, pp. 5-10.

14.

Abdul-Bary Raouf Suleiman and Toka Abdul-Hameed Fatehi, "Features Extraction Techniques of EEG Signal for BCI Applications," 2011, pp. 1-5.

15.

Yucel Kocyigit, Ahmet Alkan and Halil Erol, "Classification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network," J Med Syst, vol. 32, no. 1, 2008, pp. 17-20.

16.

Umut Orhan, Mahmut Hekim and Mahmut Ozer, "EEG Signals Classification Using the K-means Clustering and a Multilayer Perceptron Neural Network Model," Expert Systems with Applications, vol. 38, no. 10, 2011, pp. 13475-13481.

17.

Elif Deryaibeyli, "Least Squares Support Vector Machine Employing Model-based Methods Coefficients for Analysis of EEG signals," Expert Systems with Applications, vol. 37, no. 1, 2010, pp. 233- 239.

18.

Elif Derya Ubeyli, "Lyapunov Exponents/Probabilistic Neural Networks for Analysis of EEG Signals," Expert Systems with Applications, vol. 37, no. 3, 2010, pp. 985-992.

19.

Hasan Ocak, "Optimal Classification of Epileptic Seizures in EEG Using Wavelet Analysis and Genetic Algorithm," Signal Processing, vol. 88, no. 7, 2008, pp. 1858-1867.

20.

Ling Guo, Daniel Rivero, Jose A.Seoane and Alejandro Pazos, "Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks," In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, 2009, pp. 177-184.

21.

Sharanreddy. M and P.K. Kulkarni, "Review of Significant Research on EEG based Automated Detection of Epilepsy Seizures & Brain Tumor," International Journal of Scientific & Engineering Research, vol. 2, no. 8, August 2011, pp. 1- 9.

22.

Elif Derya Ubeyli, "Wavelet/Mixture of Experts Network Structure for EEG Signals Classification," Expert Systems with Applications, vol. 34, no. 3, 2008, pp. 1954-1962.

23.

EEG time series available at http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html.

24.

V. P. Nigam and D. Graupe, "A Neural-Networkbased Detection of Epilepsy," Neurol. Res., vol. 26, no. 6, 2004, pp. 55-60.

25.

V. Srinivasan, C. Eswaran, and N. Sriraam, "Artificial Neural Network based Epileptic Detection using Time-Domain and Frequency Domain Features," J. Med. Syst., vol. 29, no. 6, 2005, pp. 647-660.

26.

N. Kannathal, M. L. Choo, U. R. Acharya, and P. K. Sadasivan, "Entropies for Detection of Epilepsy in EEG," Comput. Methods Prog. Biomed., vol. 80, no. 3, 2005, pp. 187-194.

27.

N. Kannathal, U. R. Acharya, C. M. Lim, and P. K. Sadasivan, "Characterization of EEG-A Comparative Study," Comput. Methods Prog. Biomed., vol. 80, no. 1, 2005, pp. 17-23.

28.

K. Polat and S. Gunes, "Classification of Epileptiform EEG using a Hybrid System based on Decision Tree classifier and Fast Fourier Transform," Appl. Math. Comput., vol. 32, no. 2, 2007, pp. 625-631.

29.

A. Subasi, "Signal Classification using Wavelet Feature Extraction and Mixture of Expert Model," Exp. Syst. Appl., vol. 32, no. 4, 2007, pp. 1084-1093.

30.

N. F. Guler, E. D. Ubeyli, and I. Guler, "Recurrent Neural Networks Employing Lyapunov Exponents for EEG Signals Classification," Exp. Syst. Appl., vol.29, no. 3, 2005, pp. 506-514.

31.

N. Sadati, H. R. Mohseni, and A. Magshoudi, "Epileptic Seizure Detection Using Neural Fuzzy Networks," in Proc. IEEE Int. Conf. Fuzzy Syst., Jul. 16-21, 2006, pp. 596-600.

32.

I. Guler and E. D. Ubeyli, "Adaptive Neuro-Fuzzy Inference System for Classification of EEG Signals using wavelet coefficients," J. Neurosci. Methods, vol. 148, no. 2, 2005, pp. 113-121.

33.

E. D. Ubeyli and I. Guler, "Features Extracted by Eigenvector Methods for Detecting Variability of EEG Signals," Pattern Recognit. Lett., vol. 28, no. 5, 2007, pp. 592-603.

34.

T. Tzallas, "Epileptic Seizure Detection in EEGs using Time-Frequency Analysis," in IEEE transactions on Information Technology, vol. 13 no. 5, September 2009, pp. 703-710.

35.

M. Hosoz, et al., "An Adaptive Neuro-Fuzzy Inference System Model for Predicting the Performance of a Refrigeration System with a Cooling Tower," Expert Systems with Applications, vol. 38, no. 11, 2011, pp. 14148-14155.

36.

M. S. Mercy, "Performance Analysis of Epileptic Seizure Detection Using DWT & ICA with Neural Networks," International Journal Of Computational Engineering Research, vol. 2, no. 4, 2012, pp. 1109-1113.

37.

S. Hosseini, et al., "Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition," International Journal of Intelligent Systems and Applications (IJISA), vol. 5, no. 6, 2013, pp. 41-46.