Abstract
Visual detection of epileptic seizure from EEG signal is being inefficient and time consuming. Computational EEG signal analysis techniques were then used in the diagnosis and management of epileptic seizures. In this study, we compared the performance of Discrete Wavelet Transform (DWT) and the Stationary Wavelet Transform (SWT) decomposition techniques with 22 wavelet functions (Coiflets (coif), Daubechies (DB) and Symlets (Sym) families) using support vector machine classifier. We used multichannel EEG dataset of the University of Bon Epilepsy Center. From this dataset, five statistical wavelet features: max, min, average, mean of absolute and standard deviation were extracted. In all of the wavelet functions except three, in the Coiflets family, the experimental result showed that SWT achieved better classification accuracy than DWT. SWT and DWT decomposition techniques registered 99.5% and 97.5% highest classification accuracies, respectively.
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
Akareddy, S.M., Kulkarni, P.: EEG signal classification for epilepsy seizure detection using improved approximate entropy. Int. J. Public Health Sci. (IJPHS) 2, 23–32 (2013)
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 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 64, 061907 (2001)
Chen, D., Wan, S., Bao, F.S.: EEG-based seizure detection using discrete wavelet transform through full-level decomposition. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1596–1602 (2015)
Chen, D., Wan, S., Xiang, J., Bao, F.S.: A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG. PLoS ONE 12, e0173138 (2017)
Gajic, D., Djurovic, Z., Di Gennaro, S., Gustafsson, F.: Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng.: Appl. Basis Commun. 26, 1450021 (2014)
Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193, 156–163 (2010)
Juarez-Guerra, E., Alarcon-Aquino, V., Gomez-Gil, P.: Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks. In: Elleithy, K., Sobh, T. (eds.) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. LNCS, vol. 312, pp. 261–269. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-06764-3_33
Kabir, E., Zhang, Y.: Epileptic seizure detection from EEG signals using logistic model trees. Brain Inform. 3, 93–100 (2016)
Kalbhor, S.D., Harpale, V.K.: The review of detection and classification of epileptic seizures using wavelet transform. In: 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1–5 (2016)
Mahdi, M.T.O.: A new fast epilepsy detection method using electroencephalogram signal processing. World Appl. Sci. J. 14, 1119–1124 (2011)
Mallat, S.G.: A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
Orosco, L., Correa, A.G., Laciar, E.: a survey of performance and techniques for automatic epilepsy detection. J. Med. Biol. Eng. 33, 526–537 (2013)
Pellegrino, G.: Analysis for automatic detection of epileptic seizure from EEG signals. Laurea Specialistica in Bioingegneria (2014). http://tesi.cab.unipd.it/46118/1/ThesisBio.pdf
Prince, P.G.K., Hemamalini, R.R., Kumar, S.: Epileptic seizure detection using EEG signals by means of stationary wavelet transforms. IJCTA, 291–329 (2016). https://healthdocbox.com/Epilepsy/69751917-Epileptic-seizure-detection-using-eeg-signals-by-means-of-stationary-wavelet-transforms.html
Radmehr, M., Anisheh, S.M.: EEG spike detection using stationary wavelet transform and time-varying autoregressive model. Int. J. Comput. Appl. 83(13) (2013)
Shete, S., Shriram, R.: Comparison of sub-band decomposition and reconstruction of EEG signal by Daubechies9 and Symlet9 wavelet. In: 2014 Fourth International Conference on Communication Systems and Network Technologies (CSNT). IEEE (2014)
Shi, Y., Wang, S., Ying, J., Zhang, M., Liu, P., Zhang, H., et al.: Correlates of perceived stigma for people living with epilepsy: a meta-analysis. Epilepsy Behav. 70, 198–203 (2017)
Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Massachusetts Institute of Technology, 2 October 2009
Tibdewal, M.N., Dey, H.R., Mahadevappa, M., Ray, A.K., Malokar, M.: Wavelet transform based multiple features extraction for detection of epileptic/non-epileptic multichannel EEG. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1648–1653 (2016)
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time? Frequency analysis. IEEE Trans. Inf Technol. Biomed. 13, 703–710 (2009)
Tzallas, A.T., et al.: Automated epileptic seizure detection methods: a review study. In: Epilepsy-Histological, Electroencephalographic and Psychological Aspects. InTech (2012)
Zandi, A.S., Javidan, M., Dumont, G.A., Tafreshi, R.: Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57, 1639–1651 (2010)
Misiti, M., Misiti, Y., Oppenheim, G., Michel, J.P.: Wavelet toolbox: for use with MATLAB. citeulike.org (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shiferaw, G., Mamuye, A., Piangerelli, M. (2019). Stationary Wavelet Transform for Automatic Epileptic Seizure Detection. In: Mekuria, F., Nigussie, E., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-26630-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-26630-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26629-5
Online ISBN: 978-3-030-26630-1
eBook Packages: Computer ScienceComputer Science (R0)