Analysis of rat electroencephalogram during slow wave sleep and transition sleep using wavelet transform

The dynamic features of rat EEGs collected during slow wave sleep (SWS) and transition sleep (TS) were investigated in both time and frequency domains using wavelet transform based on multi-resolution signal decomposition. EEGs of freely moving rats were recorded with implanted electrodes and then decomposed into four components of delta, theta, alpha and beta using wavelet transform. The power and power percentage of each component were calculated as functions of time. In SWS EEGs, the results showed that there existed as much as 26.2% +/- 7.7% time duration in which the delta power percentage was less than 50%. In addition, the powers of other three components in small delta EEGs were significantly larger than those in large delta EEGs. This result revealed a reciprocal relationship between delta oscillation and spindle oscillation. Comparatively, the conventional method of FFT based power spectrum could only show a delta power-dominating (70.6% +/- 6.4%) spectrum of SWS EEGs. In the non-stationary TS EEG, spindle and non-spindle segments were distinguished based on the wavelet components of theta and alpha, and then the average duration of the spindles was estimated. In conclusion, the wavelet transform may be useful in developing novel quantitative time-frequency measures of sleep EEGs as valuable complements of conventional FFT method to analyze the transient changes in sleep EEGs induced by physiological, pathological or pharmacological conditions.