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Analysis of Electroencephalogram for the Recognition of Epileptogenic Area Using Ensemble Empirical Mode Decomposition

  • Gurwinder Singh
  • Birmohan Singh
  • Manpreet Kaur
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

Recognizing the epileptogenic area of a brain is done by analyzing the electroencephalogram signal. This area is responsible for the occurrence of seizure activity in a brain. In this paper, a methodology has been presented for the analysis of electroencephalogram to recognize epileptogenic area of brain. Ensemble empirical mode decomposition (EEMD) has been used for the estimation of intrinsic mode functions (IMFs), and six parameters consisting of statistical and frequency-based feature have been extracted from first ten IMFs. The ReliefF algorithm has been used to select the relevant features for the training of artificial neural network (ANN) for recognition of epileptogenic area. The methodology has been evaluated based on accuracy, specificity and sensitivity. The comparison has also been made with other methods of epileptogenic area detection where it has been observed that the proposed method outshines other.

Keywords

Epileptogenic Ensemble empirical mode decomposition Intrinsic mode function ReliefF Artificial neural network 

References

  1. 1.
    Dastidar, S. G., Adeli, H., & Dadmehr, N. (2007). Mixed band wavelet chaos neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering, 54, 1545–1551.CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Hassan, A. R., & Subasi, A. (2016). Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Computer Methods and Programs in Biomedicine, 136, 65–77.CrossRefGoogle Scholar
  4. 4.
    Greenfield, L. J., Geyer, J. D., & Carney, P. R. (2012). Reading EEGs: A practical approach. Lippincott Williams & Wilkins.Google Scholar
  5. 5.
    Menon, V., & Crottaz-Herbette, S. (2005). Combined {EEG} and f{MRI} studies of human brain function. International Review of Neurobiology, 66, 291–321.Google Scholar
  6. 6.
    Zanzotto, F. M., & Croce, D. (2010). Comparing EEG/ERP-like and fMRI-like techniques for reading machine thoughts. In International Conference on Brain Informatics (pp. 133–144).CrossRefGoogle Scholar
  7. 7.
    Das, A. B., & Bhuiyan, M. I. H. (2016). Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomedical Signal Processing and Control, 29, 11–21.CrossRefGoogle Scholar
  8. 8.
    Sharma, R., Kumar, M., Pachori, R. B., & Acharya, U. R. (2017). Decision support system for focal EEG signals using tunable-Q wavelet transform. Journal of Computer Science, 20, 52–60.Google Scholar
  9. 9.
    Bhattacharyya, A., Pachori, R. B., & Acharya, U. R. (2017). Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy, 19.CrossRefGoogle Scholar
  10. 10.
    Chen, D., Wan, S., & Bao, F. S. (2017). Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 413–425.CrossRefGoogle Scholar
  11. 11.
    Sharma, R., Pachori, R. B., & Rajendra Acharya, U. (2015). An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy, 17, 5218–5240.CrossRefGoogle Scholar
  12. 12.
    Singh, G., Kaur, M., & Singh, D. (2016). Detection of epileptic seizure using wavelet transformation and spike based features. In 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015 (pp. 1–4).Google Scholar
  13. 13.
    Sharma, R., Pachori, R. B., & Gautam, S. (2014). Empirical mode decomposition based classification of focal and non-focal EEG signals. In 2014 International Conference on Medical Biometrics (pp. 135–140).Google Scholar
  14. 14.
    Sharma, R., Pachori, R. B., & Acharya, U. R. (2015). Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy, 17, 669–691.CrossRefGoogle Scholar
  15. 15.
    Rai, K., Bajaj, V., & Kumar, A. (2015). Features extraction for classification of focal and non-focal EEG signals. Lecture Notes in Electrical Engineering, 339, 599–605.CrossRefGoogle Scholar
  16. 16.
    Kaur, M., & Singh, G. (2017). Classification of seizure prone EEG signal using amplitude and frequency based parameters of intrinsic mode functions. Journal of Medical and Biological Engineering, 37, 540–553.CrossRefGoogle Scholar
  17. 17.
    Das, A. B., & Bhuiyan, M. I. H. (2016). Discrimination of focal and non-focal EEG signals using entropy-based features in EEMD and CEEMDAN domains. In 9th International Conference on Electrical and Computer Engineering (pp. 435–438).Google Scholar
  18. 18.
    Andrzejak, R. G., Schindler, K., & Rummel, C. (2012). Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Physical Review E, 86, 046206.CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Yadav, R., Shah, A. K., Loeb, J. A., Swamy, M. N. S., & Agarwal, R. (2012). Morphology-based automatic seizure detector for intercerebral EEG recordings. IEEE Transactions on Biomedical Engineering, 59, 1871–1881.CrossRefGoogle Scholar
  21. 21.
    Wu, Z., & Huang, N. E. (2005). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41.CrossRefGoogle Scholar
  22. 22.
    Bajaj, V., & Pachori, R. B. (2012). Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine, 16, 1135–1142.CrossRefGoogle Scholar
  23. 23.
    van Putten, M. J., Kind, T., Visser, F., & Lagerburg, V. (2005). Detecting temporal lobe seizures from scalp EEG recordings: A comparison of various features. Clinical Neurophysiology, 116, 2480–2489.CrossRefGoogle Scholar
  24. 24.
    Borbély, A. A., & Neuhaus, H. U. (1979). Sleep-deprivation: Effects on sleep and EEG in the rat. Journal of Comparative Physiology, 133, 71–87.CrossRefGoogle Scholar
  25. 25.
    Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In European Conference on Machine Learning (pp. 171–182). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  26. 26.
    Weng, W., & Khorasani, K. (1996). An adaptive structure neural networks with application to EEG automatic seizure detection. Neural Networks, 9, 1223–1240.CrossRefGoogle Scholar
  27. 27.
    Hazarika, N., Chen, J. Z., Tsoi, A. C., & Sergejew, A. (1997). Classification of EEG signals using the wavelet transform. Signal Processing, 59, 61–72.CrossRefGoogle Scholar
  28. 28.
    Kumar, Y., Dewal, M. L., & Anand, R. S. (2014). Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal, Image and Video Processing, 8, 1323–1334.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Gurwinder Singh
    • 1
  • Birmohan Singh
    • 1
  • Manpreet Kaur
    • 2
  1. 1.Department of CSESLIETLongowalIndia
  2. 2.Department of EIESLIETLongowalIndia

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