睡眠脑电的分析与应用研究

发布时间：2018-03-28 07:42

本文选题：睡眠　切入点：脑电信号　出处：《广东工业大学》2014年硕士论文

【摘要】：现代医学认为,睡眠是一项非常重要的生理过程,睡眠使人的精神和体力得到恢复；睡眠质量的好坏与人的健康、学习、生活以及工作密切相关。失眠是睡眠障碍性疾病中最为常见的,虽然不属于严重疾病,但其影响着人们的健康、学习、生活和工作。脑电信号(EEG)是脑神经细胞的电生理活动在大脑皮层或头皮表面的总体反映。因此,研究睡眠脑电所蕴含的信息以及掌握睡眠周期的变化规律,对诊断和治疗与睡眠相关的疾病有着重大意义。睡眠脑电信号是一种非线性、非平稳信号,在不同时刻有不同的频率。按频率分布情况,脑电信号主要包含着四种不同频率的波,即δ节律波(1～4Hz)、θ节律波(4-8Hz)、α节律波(8-13Hz)、β节律波(13-30Hz)。脑电信号中节律波的提取,是研究脑电信号中不可或缺的重要环节。小波变换是信号分析的重要工具,是上世纪80年代后期发展起来的应用数学分支,是傅里叶变换的新发展。小波变换克服了傅里叶变换的局限性,在时域和频域都有很好的局部化特性。本文主要介绍了人睡眠的相关背景知识、脑电信号研究状况以及脑电信号的特点；详细介绍了小波理论方法、小波变换在脑电信号中的去噪应用、小波理论在睡眠脑电节律提取中的应用；简单阐述了睡眠分期准则、睡眠各期脑电特征以及介绍了小波包能量谱在睡眠分期中的应用研究。实验表明,小波理论的应用能够明显去除脑电信号中噪声信号,且保留了原始信号的重要信息；同时,应用小波分解及小波包分解都能够有效地提取脑电信号中各种节律波；最后,基于小波包能量谱提取的特征向量,统计其数学规律,发现能够区分睡眠各期,为睡眠分期提供了一个重要特征参数。医学学者已发现人在入睡和深睡阶段主要是脑电信号中的δ节律波和0节律波起着重要作用,因此本文最后提出一种基于脑电6和0节律波的脑电生物反馈疗法来进行对失眠患者的治疗。
[Abstract]:Modern medicine believes that sleep is a very important physiological process, sleep can restore people's mental and physical strength, the quality of sleep is good or bad, and people's health, learning, Life and work are closely related. Insomnia is the most common disease in sleep disorders. Although it is not a serious disease, it affects people's health and learning. Life and work. EEG is an overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. Therefore, to study the information contained in sleep EEG and to master the regularity of sleep cycle, It is of great significance in the diagnosis and treatment of sleep related diseases. Sleep EEG is a nonlinear, non-stationary signal with different frequencies at different times. According to the frequency distribution, EEG mainly contains four kinds of waves with different frequencies. That is, 未 rhythmic wave, 胃 rhythm wave, 伪 rhythm wave, 尾 rhythm wave, and 尾 rhythm wave. The extraction of rhythm wave from EEG signal is an indispensable and important link in the study of EEG signal. Wavelet transform is an important tool for signal analysis, and wavelet transform is an important tool for signal analysis. Wavelet transform is a branch of applied mathematics developed in the late 1980s and a new development of Fourier transform. Wavelet transform overcomes the limitation of Fourier transform and has good localization characteristics in both time and frequency domain. This paper mainly introduces the background knowledge of human sleep, the research status of EEG and the characteristics of EEG, and introduces in detail the wavelet theory and the application of wavelet transform in the denoising of EEG. The application of wavelet theory in the extraction of sleep EEG rhythm, the principle of sleep staging, the characteristics of EEG in each stage of sleep, and the application of wavelet packet energy spectrum in sleep staging are briefly described. The application of wavelet theory can obviously remove the noise signal in the EEG signal and retain the important information of the original signal. At the same time, the wavelet decomposition and wavelet packet decomposition can effectively extract all kinds of rhythmic waves in the EEG signal. Based on the feature vector extracted by wavelet packet energy spectrum and its mathematical rules, it is found that it can distinguish the sleep stages and provide an important characteristic parameter for sleep stages. Medical scholars have found that the 未 rhythm wave and 0 rhythm wave play an important role in sleep and deep sleep. Therefore, this paper proposes a EEG biofeedback therapy based on EEG 6 and 0 rhythm waves to treat insomnia patients.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.7;O174.2

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