Analysis of rat electroencephalogram under slow wave sleep using wavelet transform

The dynamic features of rat EEG collected under slow wave sleep were investigated in both time domain and frequency domain using wavelet transform based on multiresolution signal decomposition. EEGs of freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by wavelet transform. The power and power percentage of each component were calculated as functions of time. The results show that under slow wave sleep there existed as much as 26.2±7.7% of EEG period during which the delta power percentage was less than 50%. The powers of other three components during small delta EEG were significantly larger than those during large delta EEG. Comparatively, the conventional FFT method could only show a delta power (percentage of 70.6±6.4%) dominating spectrum. Therefore, the method of wavelet transform is useful in developing new quantitative time-frequency measures of EEG.