Abstract
This paper presents a method of feature extraction to detect seizure in epileptic patients . Epileptic seizures are characterized by high amplitude and synchronized electrocephalogram (EEG) waveforms. Power spectral density (PSD) of the EEG signal plays an important role in diagnosis of epilepsy. Many automated diagnostic systems for epileptic seizure detection have emerged in recent years. This paper proposes a method of extracting PSD of EEG sub-bands using low-complex PSD estimation method which would reduce the automatic diagnostic system complexity and also enhances the speed. Low-complexity PSD estimation method was implemented in digital signal processor (TMS320C6713), and the result was very much similar to traditional Welch PSD estimation method with 30 % reduction in computation time.
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
P.D. Welch, The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 70–73 (1967)
K.K. Parhi, M. Ayinala, Low-complexity welch power spectral density computation. IEEE Trans. Circuit Syst. (2013)
K. Barbé, R. Pintelon, J. Schoukens, Welch method revisited: Nonparametric power spectrum estimation via circular overlap. IEEE Trans. Sig. Process. 58(2), 553–556 (2010)
S.-F. Liang, H.-C. Wang, W.-L. Chang, Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process (2010)
S.R. Mousavi, M. Niknazar, B.V. Vahdat, in Epileptic Seizure Detection Using AR Model on EEG Signals. Proceedings of Cario International Biomedical Engineering Conference (CIBEC ’08), 1–4 Dec 2008
B.C. Kuo, D.A. Landgrbe, Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 42, 1096–1105 (2004)
T.I. Laakso, V. Valimaki, M. Karjalainen, U.K. Laine, Splitting the unit delay [FIR/all pass filters design]. IEEE Signal Process. Mag. 13(1), 30–60 (1996)
E. Hermanowicz, Explicit formulas for weighing coefficients of maximally flat tunable FIR delayers. Electron. Letters. 28(20), 1936–1937 (1992)
G. Garg, S. Behl, V. Singh, Assessment of non-parametric and parametric PSD estimation methods for automated epileptic seizure detection. J. Comput. 3(5), 160–163 (2011)
http://epileptologie-bonn.de/cms/front_content.php?idcat=193
R.G. Andrzejak, K. Lehnertz, C. Rieke, Indications of non linear deterministic and finite dimensional structures in time series of brain electrical activity dependence on recording region and brain state. Phys.Rev.E. 64 (2001)
http://pub.ist.ac.at/~schloegl/publications/MDBC_classifier.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Balasaraswathy, N., Rajavel, R. (2015). Low-complexity Power Spectral Density Estimation. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 325. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2135-7_30
Download citation
DOI: https://doi.org/10.1007/978-81-322-2135-7_30
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2134-0
Online ISBN: 978-81-322-2135-7
eBook Packages: EngineeringEngineering (R0)