Fig. 1 Electroencephalograms in normal brain for two 3s continuous EEG Signals. It depicts the EEG of the brain under normal condition which is chaotic and irregular.
Fig. 2 Electroencephalograms in patients with epilepsy for two 3s continuous EEG Signals. It depicts the EEG of the brain during Epilepsy attack. It has higher amplitude and rhymic pattern than under normal condition.
Fig. 3 System structure. The system consist of two major parts:- Dimensionality reduction methods and classifiers. The dimensionality reduction methods include PCA, KPCA and LDA while the classifiers used include SVM, LR, KNN, DT, and R
Fig. 4 General flow chart. The features from the dataset are extracted using discrete wavelet transform then feature selection were conducted using three dimensionality reduction methods before passing the data to classifiers.
Fig. 5 Comparison of the performance of dimensionality reduction methods and descret wave transformation in classification
Fig. 6 Comparing epileptic EEG classification results with three dimension reduction methods
Table 1. The composition of training set sample points and test set sample points
Table 2. Accuracy of dimensionality reduction methods with classifiers
References
- L. Rrambaiolli, N. Spolaor, and A. Corena, "Feature selection before EEG classification supports the diagnosis of Alzheimer's disease," Clinical Neurophysiology, vol. 128, no. 10, 2017, pp. 2058-2067. https://doi.org/10.1016/j.clinph.2017.06.251
- S. Moshe, E. Perucca, P. Ryvlin, and T. Tomson, "Epilepsy: new advances," The Lancet, vol. 385, Issue 9971, Mar. 2015, pp. 884-898. https://doi.org/10.1016/S0140-6736(14)60456-6
- J. Zhao, W. Zhou, K. Liu, and D. Cai, "EEG Signal Classification Method Based on Svm And Wavelet Analysis,"Computer Applications & Software, vol. 28, no. 5, 2011, pp. 114-116. https://doi.org/10.3969/j.issn.1000-386X.2011.05.034
- J. Jo "Drone Based Sensor Network Scenario for the Efficient Pedestrian's EEG Signal Transmission", J. of the Korea Institute of Electronic Communication Sciences, Sept. vol. 11, no. 9, 2016, pp. 923-928. https://doi.org/10.13067/JKIECS.2016.11.9.923
- Y. Jang "Analysis of Concentration-Related EEG Component Due to Smartphone", J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 7, July 2016, pp. 717-722. https://doi.org/10.13067/JKIECS.2016.11.7.717
- G. Kumar and P. Bhatia, "A Detailed Review of Feature Extraction in Image Processing Systems," Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak, India, Feb. 2014, pp. 5-12.
- R. Davidson, D. Jackson, and C. Larson, Human electroencephalography, Handbook of Psychophysiology, New York. Cambridge University Press, 2000.
- M. Nurujjaman, R. Narayanan, and A. Sekar Iyengar, "Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients," Nonlinear Biomedical Physics, vol. 3, no. 1, 2009, pp. 1-5. https://doi.org/10.1186/1753-4631-3-1
- R. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, and C. 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 E, vol. 64, 061907, Nov. 2001, pp. 1-8. view
- Q. Cai , H. Chen, and L. Xie, "Analysis of EEG Based on Improvement Wavelet Transform," Computer Technology & Development, 2008.
- J. Costa, P. Da-Silva, R. Almeida, and A. Infantosi, "Validation in Principal Components Analysis Applied to EEG Data," Computational and Mathematical Methods in Medicine, vol. 2014, Sep.2014, pp. 1-10.
- L. Cao, K. Chua, K. Chongc, H. Lee, and Q. M. Gu, "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine," Neurocomputing, vol. 55, issues 1-2, Sept. 2003, pp. 321-336. https://doi.org/10.1016/S0925-2312(03)00433-8
- A. Subasi and M. I. Gursoyb, "EEG signal classification using PCA, ICA, LDA and support vector machines," Expert Systems with Applications, vol. 37, issue 12, Dec. 2010, pp. 8659-8666. https://doi.org/10.1016/j.eswa.2010.06.065