Abstract
Marked by unpredictable seizures, epilepsy is the fourth most prevailing chronic neural disorder. This neurodegenerative disorder can attack individuals belonging to any category or age group. Also, the resulting seizures can be of any type. There is always a possibility of misjudging the symptoms with psychogenic nonepileptic events. Thus, in addition to the common methods like functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), electroencephalography (EEG) is a useful tool to differentiate epilepsy from other neurodegenerative disorders. However, EEG measures brain activity directly unlike the other two techniques that measure changes in blood flow to a certain part of the brain. Hence, EEG is most widely used. The paper focuses on conversion of time domain brain signals into time–frequency domain using wavelet transforms followed by extraction of various statistical and nonlinear features. These features are then fed to the neurons of an artificial neural network (ANN) which indicates the presence of epilepsy in an individual.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Epilepsy Foundation, http://www.epilepsy.com
Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2016) Feature extraction of epilepsy EEG using discrete wavelet transform. In: 12th international computer engineering conference (ICENCO), pp 190–195
Kolekar MH (2014) Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals. In: International conference on medical imaging, m-health and emerging communication systems (MedCom), pp 412–416
Guo L, Rivero D, Seoane JA, Pazos A (2009) Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation, pp 177–184
(Dataset) Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) 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, http://epileptologie-bonn.de/cms/
Guler I, Ubeyli ED (2007) Multiclass support vector machines for EEG-signals classification. IEEE Trans Inf Technol Biomed xi:117–126
Omerhodzic I, Avdakovic S, Nuhanovic A, Dizdarevic Z (2010) Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. World Acad Sci Eng Technol 61:1190–1195
Cheong LC, Sudirman R, Hussin SS (2015) Feature extraction of EEG signal using wavelet transform for autism classification. ARPN J Eng Appl Sci x (19):8533–8540
Anand SV, Selvakumari RS (2013) Detection of epileptic activity in the human EEG-based wavelet transforms and SVM. Int J Eng Res Technol (IJERT) ii(1):1–6
Acknowledgements
Authors would like to thank the Centre of Epilepsy in Bonn, Germany for the data provided.
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
Chakraborty, U., Mary Lourde, R. (2019). Feature Extraction and Classification of Epileptic EEG Signals Using Wavelet Transforms and Artificial Neural Networks. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_137
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_137
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)