Using the histogram analysis method to assess the time-frequency features of rat EEG under different vigilance states.
- Author:
Zhouyan FENG
1
Author Information
1. Department of Biotechnology, College of Life Science, Zhejiang University, Hangzhou, 310027, China.
- Publication Type:Journal Article
- MeSH:
Animals;
Delta Rhythm;
Electrodes, Implanted;
Electroencephalography;
Fourier Analysis;
Image Enhancement;
methods;
Rats;
Signal Processing, Computer-Assisted;
Sleep;
physiology;
Sleep Stages;
physiology;
Sleep, REM;
physiology
- From:
Journal of Biomedical Engineering
2004;21(3):371-376
- CountryChina
- Language:Chinese
-
Abstract:
To investigate the non-stationary time-frequency features in rat Electroencephalogram (EEG) under different vigilance states, the methods of multi-resolution wavelet transform (WT) and statistical histogram analysis were used. EEGs of the freely moving rats were recorded with implanted electrodes under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement sleep (REM). The EEGs were firstly decomposed into four frequency components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the parameters of mean value, standard deviation, skewness and kurtosis of the logarithm power histograms and the power percentage histograms of each of the frequency components were calculated. The results showed that the distributions of the logarithm power histograms were not quite different from the normal distribution. However, most of the power percentage histograms were significantly different from the normal distribution. The results of one-way ANOVA indicated that there were significant differences in the parameter values of the histograms both among different states and among different frequency components. Moreover, Skewness and kurtosis of the logarithm power histograms of some characteristic waves in EEG, such as delta wave during SWS and theta wave during waking and REM, obtained high values. Thus, the histogram parameters of EEG WT components might become as quantitative measures to describe the dynamic time-frequency features of EEG.
