Abstract
Method for extracting the specified rhythms of clinical electroencephalogram (EEG) is proposed using the wavelet packet decomposition. Based on the ability of accurately resolving the signal into desired time-frequency components, EEG signals are preprocessed and decomposed into a series of rhythms for many clinical applications. Specified dynamic EEG rhythms can be accurately filtered with designed wavelet structure. In addition, we present a wavelet packet entropy method for processing of EEG signal. Both relative wavelet packet energy and wavelet packet entropy are presented as the quantitative parameter to measure the complexity of the EEG signal. Several experiments with real EEG signals are carried out to show that the proposed method excels the common discrete wavelet decomposition. The presented procedure can isolate specific EEG rhythms accurately and is also regarded as an efficient method for analyzing non-stationary signals in practice.
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
Pardey, J., Roberts, S., Tarassenko, L.: A Review of Parametric Modeling Techniques for EEG Analysis. Med. Eng. Phys. 8(1), 2–11 (1996)
Jung, T.P., et al.: Estimating Alertness from the EEG Power Spectrum. IEEE Transaction on Biomedical Engineering 44(1), 60–69 (1997)
D’Attellis, C.E., et al.: Detection of Epileptic Events in Electroencephalograms Using Wavelet Analysis. Annals of Biomedical Engineering 25, 286–293 (1997)
Blanco, S., et al.: Time-Frequency Analysis of Electroencephalograms series 2. Gabor and Wavelet Transforms. Physical Review E 54(6), 6661–6672 (1996)
Thakor, N.V., et al.: Multiresolution Wavelet Analysis of Evoked Potentials. IEEE Transactions on Biomedical Engineering 40(11), 1085–1093 (1993)
Clark, I., et al.: Multiresolution Decomposition of Non-Stationary EEG Signals: A Preliminary Study. Comput. Bio. Med. 25(4), 373–382 (1995)
Unser, M., Aldroubi, A.: A Review of Wavelets in Biomedical Applications. Proceedings of the IEEE 84(4), 626–638 (1996)
Blinowska, K.J., Durka, P.J.: Application of Wavelet Transform and Matching Pursuit to the Time-Varying EEG Signals. In: Proc. of Conf. Artif. Neural Networks in Eng., pp. 535–540 (1994)
Schiff, S.J., et al.: Fast Wavelet Transformation of EEG. Electroencephalogram and Clinical Neurophysiology 91(6), 442–455 (1994)
Tseng, S., et al.: Evaluation of Parametric Methods in EEG Signal Analysis. Med. Eng. Phys. 17(1), 71–78 (1995)
Pesquet, J., Krim, H., Carfantan, H.: Time-Invariant Orthonormal Wavelet Representations. IEEE Transaction on Signal Processing 44(8), 1964–1970 (1996)
Daubechies, I.: Orthonormal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Mathematics XII, 909–996 (1988)
Quiroga, R., et al.: Wavelet Entropy in Event-Related Potentials. A New Method Shows Ordering of EEG Oscillations. Biological Cybernetics 84, 291–299 (2001)
Sun, Z.: Continuous Condition Assessment for Bridges based on Wavelet Packets Decomposition. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 4337, pp. 357–367 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Sun, L., Chang, G., Beadle, P.J. (2007). Multiresolution of Clinical EEG Recordings Based on Wavelet Packet Analysis. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_138
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)