A New Technique for Multi Resolution Characterization of Epileptic Spikes in EEG
A technique proposed for the automatic detection
of spikes in electroencephalograms (EEG). A multi-resolution
approach and a non-linear energy operator are exploited. The
signal on each EEG channel is decomposed into three sub bands
using a non-decimated wavelet transform (WT). The WT is a
powerful tool for multi-resolution analysis of non-stationary signal
as well as for signal compression, recognition and restoration.
Each sub band is analyzed by using a non-linear energy operator,
in order to detect spikes. A decision rule detects the presence of
spikes in the EEG, relying upon the energy of the three sub-bands.
The effectiveness of the proposed technique was confirmed by
analyzing both test signals and EEG layouts.
[1] M. Unser and A. Aldroubi, "A review wavelets in biomedical
applications," Proceedings of the IEEE, vol.84.pp. 626-638. April
1996.
[2] I. Clark. R. Biscay. M. Echeverria. And T. Virues, "Multiresolution
decomposition of non-stationary EEG signals: a preliminary study",
Comput. Biol. Med., vol.25, no.4 pp. 373-382, 1995.
[3] M. Vetterli and J. Kovacevic, Wavelets and Subband Coding.
Englewood Cliffs NJ: Prentice-Hall, 1995.
[4] S.G. Mallat, "A theory for multiresolution signal decomposition: The
wavelet representation," IEEE Trans. Pattern Anal. Mach. Intel.,
vol.11, pp. 674-693, July 1989.
[5] C. E. D- Attellis, S. I. Isaacson, and R. O. Sirne, "Detection of
epileptic events in electroencephalograms using wavelet analysis",
Annals of Biomed. Engng, Vol 25, pp. 286-293, 1997.
[6] F. Sartoretto and M. Ermani, "Automatic detection of epileptiform
activity by single-level wavelet analysis", Clinical
Neurophysiology, vol.110, pp. 239-249, 1999.
[7] S. Mukhopadhyay and G. C. Ray. "A new interpretation of nonlinear
energy operator and its efficacy in spike detection", IEEE Trans. On
Biomed. Engng, vol.45, no.2, 1998.
[8] E. P. Simoncelli and E. H. Adelson. "Subband transforms" in subband
Image Coding (J. W. Woods. ed.) pp. 143-192, Kluwer Academic
Publisher. 1991.
[9] Miroslaw latka & Ziemowit "Wavelet analysis of epileptic Spikes".
Wroclaw university of Technology, Poland, Dec. 22, 2002.
[1] M. Unser and A. Aldroubi, "A review wavelets in biomedical
applications," Proceedings of the IEEE, vol.84.pp. 626-638. April
1996.
[2] I. Clark. R. Biscay. M. Echeverria. And T. Virues, "Multiresolution
decomposition of non-stationary EEG signals: a preliminary study",
Comput. Biol. Med., vol.25, no.4 pp. 373-382, 1995.
[3] M. Vetterli and J. Kovacevic, Wavelets and Subband Coding.
Englewood Cliffs NJ: Prentice-Hall, 1995.
[4] S.G. Mallat, "A theory for multiresolution signal decomposition: The
wavelet representation," IEEE Trans. Pattern Anal. Mach. Intel.,
vol.11, pp. 674-693, July 1989.
[5] C. E. D- Attellis, S. I. Isaacson, and R. O. Sirne, "Detection of
epileptic events in electroencephalograms using wavelet analysis",
Annals of Biomed. Engng, Vol 25, pp. 286-293, 1997.
[6] F. Sartoretto and M. Ermani, "Automatic detection of epileptiform
activity by single-level wavelet analysis", Clinical
Neurophysiology, vol.110, pp. 239-249, 1999.
[7] S. Mukhopadhyay and G. C. Ray. "A new interpretation of nonlinear
energy operator and its efficacy in spike detection", IEEE Trans. On
Biomed. Engng, vol.45, no.2, 1998.
[8] E. P. Simoncelli and E. H. Adelson. "Subband transforms" in subband
Image Coding (J. W. Woods. ed.) pp. 143-192, Kluwer Academic
Publisher. 1991.
[9] Miroslaw latka & Ziemowit "Wavelet analysis of epileptic Spikes".
Wroclaw university of Technology, Poland, Dec. 22, 2002.
@article{"International Journal of Electrical, Electronic and Communication Sciences:64567", author = "H. N. Suresh and Dr. V. Udaya Shankara", title = "A New Technique for Multi Resolution Characterization of Epileptic Spikes in EEG", abstract = "A technique proposed for the automatic detection
of spikes in electroencephalograms (EEG). A multi-resolution
approach and a non-linear energy operator are exploited. The
signal on each EEG channel is decomposed into three sub bands
using a non-decimated wavelet transform (WT). The WT is a
powerful tool for multi-resolution analysis of non-stationary signal
as well as for signal compression, recognition and restoration.
Each sub band is analyzed by using a non-linear energy operator,
in order to detect spikes. A decision rule detects the presence of
spikes in the EEG, relying upon the energy of the three sub-bands.
The effectiveness of the proposed technique was confirmed by
analyzing both test signals and EEG layouts.", keywords = "EEG, Spike, SNEO, Wavelet Transform", volume = "2", number = "9", pages = "2085-4", }