基于小波变换的睡眠脑电信号分析

发布时间：2018-02-27 03:29

  本文关键词： 睡眠脑电 小波变换 脑电节律 小波能量 睡眠分期　出处：《南京大学》2014年硕士论文　论文类型：学位论文

【摘要】：睡眠是高等脊椎动物的一种重要的生理现象。在睡眠研究中,脑电图(EEG)是一个重要而有力的工具。脑电信号包含着众多的生理和病理信息,常用于辅助研究人类大脑活动与诊治睡眠疾患。本文简要介绍了小波变换和小波包分解的原理,用其对原始睡眠脑电信号进行去噪处理并提取基本特征节律(δ波,θ波,α波,β波)。在此基础上,对睡眠各阶段的脑电信号进行小波能量分析。脑电信号是由脑的神经系统产生的电生理反应。它与很多其他生理信号相似,幅值较小,信噪比低,为非平稳信号,且容易受到各种外界因素的干扰,如眼电、心电、肌电、白噪声等。在原始睡眠脑电信号预处理中,本文采用了小波包自适应阈值去噪的分析方法,在脑电去噪中与小波去噪相比取得了较好的效果,并采用了小波变换提取了脑电基本节律。睡眠分期的研究主要是对睡眠脑电信号中的α波和δ波进行分析,计算睡眠各期脑电节律α波和δ波的小波能量比值。由于这两个频带之间的能量比在睡眠各个阶段呈现出明显差异,并且其变化趋势具有一定的规律性。其值在清醒期最大,NREM I期、Ⅱ期不断减小,Ⅲ期、Ⅳ期的时候达到最低值,进入REM期后又回升到接近Ⅰ期值。除REM期和Ⅰ期的值比较接近外,其余各期之间分界比较明显。与人工分期结果相比较的实验结果表明,睡眠各期脑电节律α波和δ波的小波能量比值为睡眠分期提供了一种新的特征变量,在睡眠分期的研究中具有一定的实用价值。
[Abstract]:Sleep is an important physiological phenomenon in higher vertebrates. In sleep research, EEG EGG is an important and powerful tool. EEG signals contain a lot of physiological and pathological information. It is often used to assist in the study of human brain activity and the diagnosis and treatment of sleep disorders. In this paper, the principles of wavelet transform and wavelet packet decomposition are briefly introduced. The basic characteristic rhythm (未 wave, 胃 wave, 伪 wave, 尾 wave) is extracted from the denoising of the original sleep EEG signal, and the basic characteristic rhythm (未 wave, 胃 wave, 伪 wave, 尾 wave) is extracted. Wavelet energy analysis of EEG in all stages of sleep. EEG is an electrophysiological response produced by the nervous system of the brain. It is similar to many other physiological signals, with small amplitude, low signal-to-noise ratio and non-stationary signal. It is easy to be interfered by various external factors, such as eye electricity, ECG, myoelectricity, white noise and so on. In the preprocessing of original sleep EEG signal, the wavelet packet adaptive threshold de-noising method is used in this paper. Compared with wavelet de-noising, wavelet transform is used to extract the basic rhythm of EEG. The sleep stage is mainly about the analysis of 伪 wave and 未 wave in sleep EEG signal. The wavelet energy ratio of 伪 wave and 未 wave in every phase of sleep was calculated. The change trend of NREM is regular, and the value of NREM is the lowest in stage 鈪，

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