Analysis of EEG records in an epileptic patient using wavelet transform.
About 1% of the people in the world suffer from epilepsy and 30% of epileptics are not helped by medication. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records. Wavelet transform is used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. The capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals. Wavelet analyses of EEGs obtained from a population of patients can potentially suggest the physiological processes undergoing in the brain in epilepsy onset. A better understanding of the dynamics of the human brain through EEG analysis can be obtained through further analysis of such EEG records.
Adeli
Hojjat
H
Departments of Biomedical Informatics, Centers for Biomedical Engineering and Cognitive Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA. adeli.l@osu.edu
Zhou
Ziqin
Z
Dadmehr
Nahid
N
D000465:Algorithms; D019295:Computational Biology / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004832:Epilepsy, Absence / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D009462:Neurology / Q000706:statistics & numerical data