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

Zhou-Yan Feng
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Abstract

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.

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用小波变换分析大鼠慢波睡眠和过渡睡眠脑电图。
采用基于多分辨率信号分解的小波变换对大鼠慢波睡眠(SWS)和过渡睡眠(TS)的脑电图进行时域和频域动态分析。用植入电极记录自由运动大鼠的脑电图,然后用小波变换将其分解为δ、θ、α和β四个分量。以时间为函数计算各部件的功率和功率百分比。结果表明,在SWS脑电图中,存在高达26.2% +/- 7.7%的时间间隔,其中δ功率百分比小于50%。此外,小三角脑电图中其他三种成分的幂均显著大于大三角脑电图。这一结果揭示了三角振荡和主轴振荡之间的互反关系。相比之下,基于FFT的传统功率谱方法只能显示SWS脑电图的δ功率主导谱(70.6% +/- 6.4%)。在非平稳TS脑电中,根据θ和α的小波分量区分纺锤和非纺锤段,然后估计纺锤的平均持续时间。综上所述,小波变换可能有助于开发新的睡眠脑电图定量时频测量方法,作为传统FFT方法的宝贵补充,用于分析生理、病理或药理条件引起的睡眠脑电图的瞬态变化。
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