Abstract
Biomedical signal analysis can assist the diagnostic process for each neurological dysfunction. At that time the diagnostic tool continues to evolve, which is related to the development of both technological and innovative solutions proposed by scientists. One of possible solutions for automatic EEG analysis is to use modern signal processing tools, which are able to give a time-frequency representation of a signal. This paper presents a comparison of methods such as the discrete wavelet transform, the matching pursuit algorithm, and the S transform for feature extraction and then classification of neurological disorders. The research was carried out using real EEG recordings of epileptic patients as well as healthy subjects.
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
Preview
Unable to display preview. Download preview PDF.
References
Duda, R.O., Hart, P.E., Stork, D.H.: Pattern Classification, 2nd edn. Wiley Interscience (2000)
Faul, S., Boylan, G., Connolly, S.: An evaluation of automated neonatal seizure detection methods. Clin. Neurophysiol. 116, 1533–1541 (2005)
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)
James, C.J., Jones, R.D., Bones, P.J., Carroll, G.J.: Detection of epileptiform discharges in the eeg by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages. Clin. Neurophysiol. 12, 2049–2063 (1999)
Kahn, Y., Gotman, J.: Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114, 898–908 (2003)
Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Processing 41(12), 3397–3415 (1993)
McSharry, P., He, T., Smith, L.: Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings. Med. Biol. Eng. Comput. 40, 447–461 (2002)
Patan, K., Rutkowski, G.: Analysis and classification of eeg data: An evaluation of methods. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 310–317. Springer, Heidelberg (2012)
Stockwell, R.G., Mansinha, L., Lowe, R.P.: Localization of the complex spectrum: The s transform. IEEE Trans. Signal Processing 44(4), 998–1001 (1996)
Świercz, E.: Classification in the gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution. J. Applied Mathematics and Computer Science 20(1), 135–147 (2010)
Übeyli, E.D.: Combined neural network model employing wavelet coefficients for eeg signals classification. Digital Signal Processing 19, 297–308 (2009)
Woźniak, M., Krawczyk, B.: Combined classifier based on feature space partitioning. J. Applied Mathematics and Computer Science 22(4), 855–866 (2012)
Yeung, N., Bogacz, R., Holroyd, C.B., Cohen, J.D.: Detection of synchronized oscillations in the electroencephalogram: An evaluation of methods. Psychophysiology 41, 822–832 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rutkowski, G., Patan, K., Leśniak, P. (2013). Comparison of Time-Frequency Feature Extraction Methods for EEG Signals Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-38610-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38609-1
Online ISBN: 978-3-642-38610-7
eBook Packages: Computer ScienceComputer Science (R0)