Abstract
In this paper, we have introduced a new method for the classification of electroencephalogram (EEG) signals for seizure detection using feature selection and channel selection. The channels are selected based on the method of variance. Further, features have been extracted from intrinsic mode functions obtained after decomposing the EEG signal with empirical mode decomposition. The feature selection is done using a one-way analysis of variance test with a predefined threshold probability value. The classification of seizure and non-seizure signals is done on selected features using Decision Tree and k-nearest neighbor algorithms. The proposed method for classification based on a combination of feature selection and channel selection provides an accuracy of 99.6% which is better as compared to the existing methods. Thus, optimizing the number of channels and using accurate features gives us a better way of analyzing the EEG signals for seizure detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Iasemidis, L.D., Shiau, D.S., Chaovalitwongse, W., Sackellares, J.C., Pardalos, P.N., Principe, J.C., Carney, P.R., Prasad, A., Veeramani, B., Tsakalis, K.: Adaptive epileptic seizure prediction system. IEEE Trans. Biomed. Eng. 50(5), 616–627 (2003). May
Lehnertz, K., Elger, C.E.: Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogram. Clin. Neurophysiology. 95(2), 108–117 (1995)
A. Accardo, M. Affinito, M. Carrozzi, and F. Bouquet, “Use of the fractal dimension for the analysis of electroencephalographic time series,” Biol Cybern., vol. 77, pp. 339 350, 1997
Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 11(3), 288–295 (2007). May
Lay-Ekuakille, Aimé, et al. “Entropy index in quantitative EEG measurement for diagnosis accuracy.” IEEE Transactions on Instrumentation and Measurement 63.6 (2013): 1440-1450
Oweis, R.J., Abdulhay, E.W.: Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. OnLine 10, 38 (2011)
Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Progr Biomed. 104(3), 373–381 (2011)
V. Bajaj and R. B. Pachori 2012 Classification of Seizure and non-seizure EEG Signals Using Empirical Mode Decomposition, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 16, NO. 6
Liang, S.F., Wang, H.C., Chang, W.L.: Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process. 2010, 853434 (2010)
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministics and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64(6), 061907 (2001)
Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A., et al.: A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process. 2015, 66 (2015). https://doi.org/10.1186/s13634-015-0251-9
Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition ’ as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004). Feb.
Schuyler, R., White, A., Staley, K., Cios, K.J.: Epileptic seizure detection. IEEE Eng. Med. Biol. Mag. 26(2), 74–81 (2007)
Dastidar, S.G., Adeli, H., Dadmehr, N.: Mixed-band wavelet-chaos neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 54(9), 1545–1551 (2007). Sep.
Ostertagová, Eva, Ostertag, Oskar: Methodology and Application of One-way ANOVA. American Journal of Mechanical Engineering 1(7), 256–261 (2013). https://doi.org/10.12691/ajme-1-7-21
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Suman, S., Kanakavety, V.D., kattoju, A.V., Ghare, P. (2022). Classification of EEG Signals for Seizure Detection Using Feature Selection and Channel Selection. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_47
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)