Abstract
The seizure detection from an EEG signal has shown substantial potential for the improvement of accuracy and efficiency in epilepsy diagnosis. The success of the diagnosis is determined by the feature extraction step, which aims to identify meaningful patterns associated to various mental activity. During the ictal stage, which occurs during a seizure, the mental activities change. As a result, a wavelet-based strategy to extracting features from EEG data is proposed in this paper. By using wavelet analysis, the sub-bands can be obtained which corresponds to a certain frequency range. These frequency range are related to various mental activities. The EEG signal is decomposed into various sub-bands Delta, Theta, Alpha, Beta and Gamma. After that, the maximum energy and power are found for each sub-band which used as features in order to obtain descriptors. These descriptors are evaluated using different classifiers K-nearest neighbor, Quadratic Discriminant, Kernel Naïve Bayes, Gaussian support vector machine and Ensemble subspace KNN. The analysis showed that levels of Discrete Wavelet Transform and the use of time-frequency features affect the final seizure detection performance. The set A and set E from the open access dataset of Bonn University is used for testing and validation.
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
F. Mormann, R.G. Andrzejak, C.E. Elger, K. Lenhnertz, Seizure prediction: the long and the winding road. Brain 130(2), 314–333 (2007)
J. Gotman, Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16(2), 130–140 (1999)
Q.S. Mian, S. Abdulhamit, Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare. J. Ambient Intell. Hum. Comput. (2020)
W.R.S. Webber, R.P. Lesser, R.T. Richardson, K. Wilson, An approach to seizure detection using an artificial neural network (ANN). Electroenceph. Clin. Neurophysiol. 98(4), 250–272 (1996)
P.F. Prior, R.S.M. Virden, D.E. Maynard, An EEG device for monitoring seizure discharges. Epilepsia 14(4), 367–372 (1973)
V.P. Nigam, D. Graupe, A neural-network-based detection of epilepsy. Neurol. Res. 26(6), 55–60 (2004)
B. Gonzalez-Vellon, S. Sanei, J.A. Chambers, Support vector machines for seizure detection, in Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, 14–17 Dec, Germany, pp. 126–29 (2003)
H. Adeli, Z. Zhou, N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Meth. 123(1), 69–87 (2003)
N. Kannathal, U.R. Acharya, C.M. Lim, P.K. Sadasivan, Characterization of EEG-A comparative study. Comp. Meth. Prog. Biomed. 80(1), 17–23 (2005)
D.E. Lerner, Monitoring changing dynamics with correlation integrals: case study of an epileptic seizure. Physica D 97(4), 563–576 (1996)
N.F. Gler, E.D. Beyli, I. Gler, Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29(3), 506–514 (2005)
O. Faust, U. Rajendra Acharya, H. Adeli, A. Adeli, Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26, 56–64 (2015)
Epileptologie Bonn/Forschung/AG Lehnertz/EEG Data Download n.d. http://epileptologie-bonn.de/cms/front_content.phpidcat=193&lang=3. Last accessed 2020/2/3
A. Temko, G. Boylan, W. Marnane, G. Lightbody, Robust neonatal EEG seizure detection through adaptive backgroundmodeling. Int. J. Neural Syst. 23(4), 1350018 (2013)
K.C. Hsu, S.N. Yu, Detection of seizures in EEG using sub band nonlinear parameters and genetic algorithm. Comput. Biol. Med. 40, 823–830 (2010)
X.-Q. Wu, K.-Q. Wang, D. Zhang, Wavelet energy feature extraction and matching for palmprint recognition. J. Comput. Sci. Technol. 203, 411–418 (2005)
R.J. Oweis, E.W. Abdulhay, Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. Online 10, 38 (2011)
L.-Y. Hu, M.-W. Huang, S.-W. Ke, C.-F. Tsai, The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus 5, 1304 (2016)
A. Sharmila, P. Geethanjali, DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4, 7716–7727 (2016)
S. Raghu, N. Sriraam, A.S. Hegde, P.L. Kubben, A novel approach for classification of epileptic seizures using matrix determinant. Expert Syst. Appl. 127, 323–341 (2019)
V. Gupta, A. Bhattacharyya, R.B. Pachori, Automated identification of epileptic seizures from EEG signals using FBSE-EWT method, in Biomedical Signal Processing, pp. 157–179 (2020)
D. Nabil, R. Benali, F. Bereksi Reguig, Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification. Biomed. Tech. 65(2), 133–148 (2020)
D.P. Dash, M.H. Kolekar, Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform. J. Biomed. Res. 34(3), 170 (2020)
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
Meshram, P.S., Gharpure, D.C. (2022). Epileptic Seizure Detection Using Wavelet-Based Features from Different Sub-bands. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_26
Download citation
DOI: https://doi.org/10.1007/978-981-19-1324-2_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1323-5
Online ISBN: 978-981-19-1324-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)