Article Sidebar

Submitted
Apr 26, 2017
Published
Dec 1, 2012
Download
PDF
Statistic
Read Counter : 410 Download : 238

Main Article Content

Abstract

Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. EEG signals, like many biomedical signals, are highly non-stationary by their nature. Wavelet analysis has found a prominent position in the investigation of biomedical signals for its ability to analyze such signals, in particular EEG signals. Wavelet transform is capable of separating the signal energy among different frequency bands (i.e., different scales), achieving a good compromise between temporal and frequency resolution. The present study is an attempt at better understanding of the mechanism causing the epileptic disorder and accurate prediction of the occurrence of seizures. In the present paper we identify typical patterns of energy redistribution before and during a seizure using multi-resolution wavelet analysis.

 

 

Keywords

Electroencephalography Epilepsy Multi-resolution Neuroscience Power spectral density Signal energy Wavelet.

Article Details

References

  1. ADDISON, P.S. 2002. The Illustrated Wavelet Transform Handbook – Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Institute of Physics Publishing, Bristol and Philadelphia.
  2. ADELI, H., ZHOU, Z. and DADMEHR, N. 2003. Analysis of EEG records in epileptic patient using wavelet transform. J. Neurosci Methods, 123: 69-87.
  3. ALDROUBI, A. and UNSER, M.A. 1996. Wavelets in Medicine and Biology. CRC Press.
  4. ATTELIS, C.E., ISAACSON, S.I. and SIRNE, R.O. 1997. Detection of epileptic events in Electroencephalogram using wavelet analysis. Ann. Biomed. Eng., 25: 286-293.
  5. BHANDARI, A., KHARE, V., SANTHOSH, J. and ANAND, S. 2007. Wavelet based compression technique of Electroculogram signals. IFMBE Proceedings 15: 440-443.
  6. CHUI, C.K. 1992. An Introduction to Wavelets. Academic Press, San Diego, USA.
  7. DAUBECHIES, I. 1990. The wavelet transform: Time-frequency localization and signal analysis. IEEE Trans. Inform. Theor. 36: 961-1005.
  8. FISHER, R.S., WEBBER, W.R., LESSER, R.P., ARROYO, S. and UEMATSU, S. 1992. High-frequency EEG activity at the start of seizures. J. Clin. Neurophysiol. 9: 441-448.
  9. FURATI, K.M., NASHED, M.Z. and SIDDIQI, A.H. 2006. (eds.) Mathematical Models and Methods for Real World System. Chapman and Hall ICRC, Taylor and Francis Group, Boca Raton, USA.
  10. GENCAY, R., SELUK, F. and WHICHTER, B. 2002. An Introduction to Wavelet and Filtering Methods in Finance and Economics. Academics Press, San Diego, USA.
  11. GIGOLA, S., ORTIZ, F., ATTELIS, C.E.D., SILVA, W. and KOCHEN, S. 2004. Prediction for epileptic seizure using accumulated energy in a MRA frame work. J. Neurosci Methods 138: 107-111.
  12. INOUYE, T., MATSUMOTO, Y., SHINOSAKI, K., IYAMA, A. and TOI, S. 1994. Increases in the power spectral slope of background Electroencephalogram just prior to asymmetric spike and wave complexes in Epileptic patients. Neuroscience Letters 173: 197-200.
  13. INOUYE, T., SAKAMOTO, H., SHLNOSAKA, K., TOI, S. and UKAL, S. 1990. Analysis of rapidly changing EEGs before generalized spike and wave complexes. Electroencephalography and Chmcal Neurophystology, 76: 205-221.
  14. ISKE, A. and RANDEN, T. 2006. (eds.) Mathematical Methods and Modeling in Hydrocarbon Exploration and Production. Springer-Schlumberger, Berlin, Germany.
  15. LATKA, M., WAS, Z. KOZIK, A. and WEST, B.J. 2003. Wavelet analysis of Epileptic spikes. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 67: 052902.
  16. MAGOSSO, E., URSINO, M., ZANIBONI, A. and GARDELLA, E. 2009. A wavelet based energetic approach for the analysis of biomedical signals. J. Applied Mathematics and Computation, 207: 42-62.
  17. MALLAT, S.G. 1989. A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11:674-693
  18. MANCHANDA, P., KUMAR, J., KHENE, F. and SIDDIQI, A.H. 2007. In Siddiqi, A.H., Duff, I.S. and Christensen, O. (eds). Modern Mathematical Models, Methods and Algorithm For Real World System. Anshan and Anamaya Publishing, New Delhi, India.
  19. NINDS 2001. Seizure and Epilepsy: Hope Through Research. National Institute of Neurological Disorders and Stroke (NINDS). Available from:
  20. http://www.ninds.nih.gov/health_and_medical/pubs/seizures_and_epilepsy_htr.htm.
  21. PERCIVAL, D.B. and WALDEN, A.T. 2000. Wavelet Methods for Time Series Analysis. Cambridge University Press, Cambridge, UK.
  22. RIVERA, N. 2003. Reservoir Characterization Using Wavelet Transform. Ph.D. Dissertation, Texas A & M University.
  23. ROSSO, O.A., MARTIN, M.T., FIGLIOLA, A., KELLER, K. and PLASTINO, A. 2006. EEG analysis using wavelet based information tools. J. Neurosci. Methods, 153: 163-182.
  24. SIDDIQI, A.H., 2004. Applied Functional Analysis. Marcel Dekker, New York, USA.
  25. SIDDIQI, A.H., DUFF, I.S. and CHRISTENSEN, O. 2007. Modern Mathematical Models, Methods and Algorithms For Real World System. Anshan and Anamaya, New Delhi, India.
  26. SIDDIQI, A.H., CHANDIOK, A. and BHADOURIA, V.S. 2009. Analysis and prediction of energy distribution in Electroencephalogram (EEG) using wavelet transform. Proc. 4th International Workshop on Wavelets, Kocaeli University, Turkey, 5-6 June 2009.
  27. YUE, W., TAO, G. and LIU, Z. 2005. Identifying reservoir fluids by wavelet transform of well logs. Journal of Petroleum Technology, 57(5): 53-54.
  28. YUE, W. and TAO, G. 2006. Identifying reservoir fluids by wavelet transform of well logs. SPE Reservoir Evaluation and Engineering, 574-581.