Epileptic Electroencephalogram Classification using Relative Wavelet Sub-Band Energy and Wavelet Entropy

Document Type : Original Article

Authors

1 School of Applied Science, Telkom University, Bandung, Indonesia

2 School of Electrical Engineering, Telkom University, Bandung, Indonesia

Abstract

Epilepsy is one of the common neurological disorders which can cause unprovoked seizures. Currently, diagnosis and evaluation are carried out using electroencephalogram (EEG) signal analysis, which is performed visually by clinicians. Since EEG signals tend to be random and non-stationary, the visual inspection often provides misrepresentation of results. Numerous studies have been proposed computer-based analysis for epileptic EEG classification; however, there is still a gap to improve detection accuracy with a small number of features. Therefore, in this study, we proposed an automatic detection protocol for epileptic EEG classification. The proposed methods are relative wavelet energy and wavelet entropy for feature extraction and combined with the classifier method for automatic detection. In this study, three classes of EEG consisted of pre-ictal, ictal, and interictal were used as test data and also evaluate the proposed method. EEG signals were decomposed using wavelet transform into five conventional sub-bands, including gamma, beta, alpha, theta, and delta. The relative energy and entropy were then calculated in each of these bands as a feature set. These methods are chosen with consider of low-cost computing. We tested the performance of our feature extraction method using Support Vector Machine (SVM), both linear and non-linear kernels. From the simulation, the highest accuracy was 80-96.7% for ictal vs. pre-ictal, ictal vs. inter-ictal, pre-ictal vs. inter-ictal, and ictal vs. non-ictal. Finally, this work was expected to help clinicians in the detection of epilepsy onset based on EEG signals.

Keywords

References

  1. Krook-Magnuson, E. and Soltesz, I. "Beyond the hammer and the scalpel: Selective circuit control for the epilepsies." Nature Neuroscience, Vol. 18, No. 3, (2015), 331-338. doi: 10.1038/nn.3943
  2. Malmivuo, J. and Plonsey, R., Bioelectromagnetism - Principles and Applications of Bioelectric and Biomagnetic Fields. New York: Oxford University Press, 1995.
  3. Acharya, U. R., Hagiwara, Y., Deshpande, S. N., Suren, S., Koh, J. E. W., Oh, S. L., … Lim, C. M. “Characterization of focal EEG signals: A review.” Future Generation Computer Systems, Vol. 91, (2019), 290-299. doi: 10.1016/j.future.2018.08.044
  4. Faust, O., Acharya, U. R., Adeli, H., and Adeli, A. “Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.” Seizure, Vol. 26, (2015), 56-64. doi: 10.1016/j.seizure.2015.01.012
  5. Khanmohammadi, S. and Chou, C. A. “Adaptive Seizure Onset Detection Framework Using a Hybrid PCA-CSP Approach.” IEEE Journal of Biomedical and Health Informatics, Vol. 22, No. 1, (2018), 154-160. doi: 10.1109/JBHI.2017.2703873.
  6. Carney, P. R., Myers, S. and Geyer, J. D. “Seizure prediction: Methods.” Epilepsy and Behavior, Vol. 22(SUPPL. 1), (2011), S94-S101. doi: 10.1016/j.yebeh.2011.09.001.
  7. Altunay, S., Telatar, Z. and Erogul, O. “Epileptic EEG detection using the linear prediction error energy.” Expert Systems with Applications, Vol. 37, No. 8, (2010), 5661-5665. doi: 10.1016/j.eswa.2010.02.045.
  8. Sugianela, Y., Sutino, Q. L. and Herumurti, D. “Eeg Classification for Epilepsy Based on Wavelet Packet Decomposition and Random Forest.” Jurnal Ilmu Komputer dan Informasi, Vol. 11 No. 1, (2018), 27-33. doi: 10.21609/jiki.v11i1.549.
  9. Guo, L., Rivero, D., Seoane, J. A., and Pazos, A. “Classification of EEG signals using relative wavelet energy and artificial neural networks”, in 2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC’09, (2009). doi: 10.1145/1543834.1543860.
  10. Sik, H. H., Gao, J., Fan, J., Wu, B. W. Y., Leung, H. K., and Hung, Y. S. “Using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities.” Journal of Visualized Experiments, Vol. 123, (2017), 1-10. doi: 10.3791/55455.
  11. Adeli, H., Zhou, Z. and Dadmehr, N. “Analysis of EEG records in an epileptic patient using wavelet transform.” Journal of Neuroscience Methods, Vol. 123, No. 1, (2003), 69-87. doi: 10.1016/S0165-0270(02)00340-0.
  12. Lee, S. H., Lim, J. S., Kim, J. K., Yang, J., and Lee, Y. “Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance” Computer Methods and Programs in Biomedicine, Vol. 116, No. 1, (2014), 10-25. doi: 10.1016/j.cmpb.2014.04.012.
  13. Dwi Saputro, I. R., Maryati, N. D., Solihati, S. R., Wijayanto, I., Hadiyoso, S., and Patmasari, R. “Seizure Type Classification on EEG Signal using Support Vector Machine.” Journal of Physics: Conference Series, Vol. 1201, No. 1, (2019). doi: 10.1088/1742-6596/1201/1/012065.
  14. Fereydouni, A. R., Charmin, A., Vahdati, H., and Aghdam, H. N. “Channel Estimation and Carrier Frequency Offset Compensation in Orthogonal Frequency Division Multiplexing System Using Adaptive Filters in Wavelet Transform Domain.” International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 7, (2020), 1231-1239. doi: 10.5829/ije.2020.33.07a.09.
  15. Khoshnood, A. M., Khaksari, H., Roshanian, J., & Hasani, S. M. “Active noise cancellation using online wavelet based control system: Numerical and experimental study.” International Journal of Engineering, Transactions A: Basics, Vol. 30, No. 1, (2017), 120-126. doi: 10.5829/idosi.ije.2017.30.01a.15.
  16. Kehtarnavaz, N. Digital Signal Processing System Design. Academic Press, 2008.
  17. Gandhi, T., Panigrahi, B. K. and Anand, S. “A comparative study of wavelet families for EEG signal classification”Neurocomputing, Vol. 74, No. 17, (2011), 3051-3057. doi: 10.1016/j.neucom.2011.04.029.
  18. Swami, P., Gandhi, T. K., Panigrahi, B. K., Tripathi, M., and Anand, S. “A novel robust diagnostic model to detect seizures in electroencephalography.” Expert Systems with Applications, Vol. 56, (2016), 116-130. doi: 10.1016/j.eswa.2016.02.040.
  19. Swami, P., Gandhi, T. K., Panigrahi, B. K., Bhatia, M., Santhosh, J., and Anand, S. “A comparative account of modelling seizure detection system using wavelet techniques.” International Journal of Systems Science: Operations and Logistics, Vol. 4, No. 1, (2017), 41-52. doi: 10.1080/23302674.2015.1116637.
  20. Irawati, I. D., Hadiyoso, S. and Hariyani, Y. S. “Multi-wavelet level comparison on compressive sensing for MRI image reconstruction.” Bulletin of Electrical Engineering and Informatics, Vol. 9, No. 4, (2020), 1461-1467. doi: 10.11591/eei.v9i4.2347.
  21. Awad, M. and Khanna, R. Support Vector Machines for Classification. Efficient Learning Machines, Berkeley, Apress, Berkeley: CA,2015
  22. Hadiyoso, S., Wijayanto, I., Rizal, A., and Aulia, S. “Biometric systems based on ECG using ensemble empirical mode decomposition and variational mode decomposition.” Journal of Applied Engineering Science, Vol. 18, No. 2, (2020) 181-191. doi: 10.5937/jaes18-26041.
  23. Martisius, I., Damasevicius, R., Jusas, V., and Birvinskas, D. “Using higher order nonlinear operators for SVM classification of EEG data.” Elektronika Ir Elektrotechnika, Vol. 3, No. 3, (2012), 99-102. doi: 10.5755/j01.eee.119.3.1373.
  24. Wijayanto, I., Rizal, A. and Hadiyoso, S. “Multilevel Wavelet Packet Entropy and Support Vector Machine for Epileptic EEG Classification.” 2018 4th International Conference on Science and Technology (ICST). Yogyakarta, (2018).
  25. Zamanian, H. and Farsi, H. “A new feature extraction method to improve emotion detection using EEG signals.” Electronic Letters on Computer Vision and Image Analysis, Vol. 17, No. 1, (2018), 9-44. doi: 10.5565/rev/elcvia.1045.
  26. Cortes, C. and Vapnik, V. Support-Vector Networks in Machine Learning, Boston: Kluwer Academic, 1995.
  27. Weng, W. C., Jiang, G. J. A., Chang, C. F., Lu, W. Y., Lin, C. Y., Lee, W. T., and Shieh, J. S. “Complexity of multi-channel electroencephalogram signal analysis in childhood absence epilepsy.” PLoS ONE, Vol. 10, No. 8, (2015), 1-14. doi: 10.1371/journal.pone.0134083.
  28. Sharma, M., Bhurane, A. A. and Rajendra Acharya, U. “MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection.” Knowledge-Based Systems, Vol. 160, (2018), 265-277. doi: 10.1016/j.knosys.2018.07.019.
  29. Gupta, A., Singh, P. and Karlekar, M. “A novel signal modeling approach for classification of seizure and seizure-free EEG signals.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, No. 5, (2018), 925-935. doi: 10.1109/TNSRE.2018.2818123.

 

 

International Journal of Engineering
Volume 34, Issue 1
TRANSACTIONS A: Basics
January 2021
Pages 75-81

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