Abstract
Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p < 0.05) and z-score (−1.96 > z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey’s range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.
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Aarabi, A., Wallois, F., and Grebe, R., Automated neonatal seizures detection: A multistage classification system through feature selection based on relevance and redundancy analysis. Clin. Neurophysiol. 117:328–340, 2006.
Aarabi, A., Grebe, R., and Wallois, F., A multistage knowledge-based system for EEG seizures detection in newborn infants. Clin. Neurophysiol. 118(12):2781–2797, 2007.
Acharya, U. R., Molinari, F., Vinitha, S. S., and Chattopadhyay, S., Automatic diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4):401–408, 2012.
Andrzejak, R. G., Schindler, K., and Rummel, C., Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E. 2012(86):046206:1–17, 2012.
Bedeeuzzaman, M., Farooq, O., and Khan, Y. U., Automatic seizures detection using higher order moments. In: Proceedings of the International Conference on Recent Trends in Information, Telecommunication and Computing, pp. 159–163 (2010)
Bogaarts, J. G., Gommer, E. D., Hilkman, D. M. W., van Kranen-Mastenbroek, V. H. J. M., and Reulen, J. P. H., Optimal training dataset composition for SVMbased, ageindependent, automated epileptic seizures detection. Med. Biol. Eng. Comput. 54:1285–1293, 2016.
Bull, A. D.: Convergence rates of efficient global optimization algorithms. 2011. arXiv:1101.3501v3
Chen, D., Wan, S., and Bao, F. S.: Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition. In: IEEE International Workshop on Machine Learning for Signal Processing. Sept. 17–20 (2015)
Christianini, N., and Shawe-Taylor, J. C., An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study. Proc. IEEE Eng. Med. Biol. Soc. Conf. 2008: 3824–3827, 2008.
Das, A. B., and Bhuiyan, M. I. H., Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Signal Process. Control 29:11–21, 2016.
Delaneya, R. C., Alexander, J. R., Mattson, R. H., and Novelly, R. A., Memory function in focal epilepsy: A comparison of non-surgical, unilateral temporal lobe and frontal lobe samples. Cortex 16(1):103–117, 1980.
Eitricha, T., and Langb, B., Efficient optimization of support vector machine learning parameters for unbalanced datasets. J. Comput. Appl. Math. 196(2):425–436, 2006.
Fan, R. E., Chen, P. H., and Lin, C. J., Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6:1889–1918, 2005.
Fawcett, T., An introduction to ROC analysis. Pattern Recogn. Lett. 27(8):861–874, 2006.
Gelbart, M., Snoek, J., and Adams, R. P.: Bayesian optimization with unknown constraints. 2014. arXiv:1403.5607
Greene, B. R., Faul, S., Marnane, W. P., Lightbody, G., Korotchikova, I., and Boylan, G. B., A comparison of quantitative EEG features for neonatal seizures detection. Clin. Neurophysiol. 119(6):1248–1261, 2008.
Guler, I., and Ubeyli, E. D., Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 2(2):117–126, 2007.
Kecman, V. Learning and soft computing. Cambridge: MIT Press, 2001.
Kecman, V., Huang, T. M., and Vogt, M.: Iterative single data algorithm for training kernel machines from huge data sets: theory and performance. In: Support Vector Machines: Theory and Applications, pp. 255–274: Springer (2005)
Krauledat, M., Dornhege, G., Blankertz, B., and Muller, K. B., Robustifying EEG data analysis by removing outliers. Chaos and Complexity Theory 2(2):259–274, 2007.
Li, W., Mo, W., and et al, Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. J Biomed Opt. 20(12):121305, 2015.
Menshawy, M. L., Benharref, A., and Serhani, M., An automatic mobile-health based approach for EEG epileptic seizures detection. Expert Systems with Applications 42:7157–7174, 2015.
Moshe, S. L., Perucca, E., Wiebe, S., and Mathern, G. W.: The International League Against Epilepsy at the threshold of its second century (2011)
Pauri, F., Pierelli, F., Chatrian, G. M., and Erdly, W. W., Long-term EEG-video-audio monitoring: computer detection of focal EEG seizures patterns. Electroencephalogr. Clin. Neurophysiol. 82(1):1–9, 1992.
Powers, D. M. W., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1):37–63, 2011.
Pravin, S., Sriraam, N., Benakop, P., and Jinaga, B., Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert System Applications 37(4):3284–3291, 2010.
Ozbeyaz, A., Gürsoy, M. I., and Oban, R., Regularization and kernel parameters optimization based on PSO algorithm in EEG signals classification with SVM. In: 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), Antalya, pp. 399–402 (2011)
Raghu, S., Sriraam, N., and Pradeep Kumar, G., Effect of wavelet packet log energy entropy on electroencephalogram (EEG) signals. International Journal of Biomedical and Clinical Engineering 4(1):32–43, 2015.
Raghu, S., Sriraam, N., and Pradeep Kumar, G., Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cognitive Neurodynamics Springer 11(1): 51–66, 2016.
Raghu, S., and Sriraam, N., Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures. Expert Systems With Applications 89:205–221, 2017.
Scholkopf, B., and Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine Learning. Cambridge: The MIT Press, 2002.
Sharma, R., and Pachori, A. U. R., Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17:669–691, 2015.
Sharma, R., Pachori, R. B., and Acharya, U. R., An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17:5218–5240, 2015.
Sharma, R., Pachori, R. B., and Gautam, S., Empirical mode decomposition based classification of focal and non-focal EEG signals. In: Proceedings of 2014 International Conference on Medical Biometrics, Shenzhen, China, pp. 135–140 (2014)
Srinivasan, V., Eswaran, C., and Sriraam, N., Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 11(3):288–295, 2007.
Winkler, I., Haufe, S., and Tangermann, M., Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behavioural and Brain Functions 7(30):1–35, 2007.
Zhu, G., Li, Y., Wen, P. P., Wang, S., and Xi, M., Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. In: Proceeding of 2013 International Symposium on Computational Models for Life Science, Sydney, Australia, Vol. 1559, p. 3136 (2013)
Acknowledgements
Authors would like to thank Dr. R.G. Andrzejak, for providing permission to use EEG database for research work. The authors would also like to thank the anonymous reviewers for their helpful comments and suggestions that greatly improved the quality and clarity of the manuscript.
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Sriraam, N., Raghu, S. Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier. J Med Syst 41, 160 (2017). https://doi.org/10.1007/s10916-017-0800-x
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DOI: https://doi.org/10.1007/s10916-017-0800-x