Analysis of EEG signal for seizure detection based on WPT

A. Arı

Analysis of EEG signal for seizure detection based on WPT

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Author(s): A. Arı¹
- Affiliations: 1: Department of Computer Engineering, Faculty of Engineering , Inonu University , 44280 Malatya , Turkey
Source: Volume 56, Issue 25, 10 December 2020, p. 1381 – 1383
DOI: 10.1049/el.2020.2701 , Print ISSN 0013-5194, Online ISSN 1350-911X

Received 15/09/2020, Published 22/10/2020

Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer-based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k-nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.

References

1. 1)
  - 1. Siuly, S., Li, Y., Wen, P.: ‘EEG signal classification based on simple random sampling technique with least square support vector machine’, Int. J. Biomed. Eng. Technol., 2011, 7, (4), pp. 390–409, (doi: 10.1504/IJBET.2011.044417).
2. 2)
  - 13. Rostaghi, M., Azami, H.: ‘Dispersion entropy: A measure for time-series analysis’, IEEE Signal Process. Lett., 2016, 23, (5), pp. 610–614, (doi: 10.1109/LSP.2016.2542881).
3. 3)
  - 14. Guo, L., Rivero, D., Dorado, J., et al: ‘Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks’, J. Neurosci. Methods, 2010, 191, (1), pp. 101–109, (doi: 10.1016/j.jneumeth.2010.05.020).
4. 4)
  - 2. Jahankhani, P., Kodogiannis, V., Revett, K.: ‘EEG signal classification using wavelet feature extraction and neural networks’. IEEE John Vincent Atanasoff 2006 Int. Symp., Sofia, Bulgaria, October 2006, pp. 120–124, doi: 10.1109/JVA.2006.17.
5. 5)
  - 4. Jiang, Y., Chen, W., Li, M.: ‘Symplectic geometry decomposition-based features for automatic epileptic seizure detection’, Comput. Biol. Med., 2020, 116, pp. 1–12, (doi: 10.1016/j.compbiomed.2019.103549).
6. 6)
  - 3. Subasi, A., Erçelebi, E.: ‘Classification of EEG signals using neural network and logistic regression’, Comput. Methods Programs Biomed., 2005, 78, (2), pp. 87–99, (doi: 10.1016/j.cmpb.2004.10.009).
7. 7)
  - 13. Yang, B., Li, H., Wang, Q., et al: ‘Subject-based feature extraction by using fisher WPD-CSP in brain–computer interfaces’, Comput. Methods Programs Biomed., 2016, 129, pp. 21–28, (doi: 10.1016/j.cmpb.2016.02.020).
8. 8)
  - 11. Andrzejak, R.G., Lehnertz, K., Mormann, F., et al: ‘Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state’, Phys. Rev. E, 2001, 64, (6), pp. 1–8, (doi: 10.1103/PhysRevE.64.061907).
9. 9)
  - 7. Al-Hadeethi, H., Abdulla, S., Diykh, M., et al: ‘Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications’, IEEE Expert Syst. Appl., 2020, 161, pp. 1–14, doi: 10.1016/j.eswa.2020.113676.
10. 10)
  - 5. Acharya, U.R., Molinari, F., Sree, S.V., et al: ‘Automated diagnosis of epileptic EEG using entropies’, Biomed. Signal Proc. Control, 2012, 7, (4), pp. 401–408, (doi: 10.1016/j.bspc.2011.07.007).
11. 11)
  - 20. Siuly, S., Alçin, Ö.F., Kabir, E., et al: ‘A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals’, IEEE Trans. Neural Syst. Rehabil. Eng., 2020, 28, (9), pp. 1966–1976, (doi: 10.1109/TNSRE.2020.3013429).
12. 12)
  - 9. Zeng, W., Li, M., Yuan, C., et al: ‘Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks’, Artif. Intell. Rev., 2020, 53, (4), pp. 3059–3088, (doi: 10.1007/s10462-019-09755-y).
13. 13)
  - 6. Murugavel, A.S., Ramakrishnan, S.: ‘An optimized extreme learning machine for epileptic seizure detection’, IAENG Int. J. Comput. Sci., 2014, 41, (4), pp. 212–221.
14. 14)
  - 10. Wu, J., Zhou, T., Li, T.: ‘Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting’, Entropy, 2020, 22, (2), p. 140, (doi: 10.3390/e22020140).
15. 15)
  - 8. Amin, H.U., Yusoff, M.Z., Ahmad, R.F.: ‘A novel approach based on wavelet analysis and arithmetic coding for automated detection and techniques’, Biomed. Signal Proc. Control, 2020, 56, pp. 1–10, doi: 10.1016/j.bspc.2019.101707.

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Analysis of EEG signal for seizure detection based on WPT

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