本文選題：睡眠　切入點：腦電信號　出處：《廣東工業(yè)大學(xué)》2014年碩士論文

【摘要】：現(xiàn)代醫(yī)學(xué)認為,睡眠是一項非常重要的生理過程,睡眠使人的精神和體力得到恢復(fù)；睡眠質(zhì)量的好壞與人的健康、學(xué)習(xí)、生活以及工作密切相關(guān)。失眠是睡眠障礙性疾病中最為常見的,雖然不屬于嚴(yán)重疾病,但其影響著人們的健康、學(xué)習(xí)、生活和工作。腦電信號(EEG)是腦神經(jīng)細胞的電生理活動在大腦皮層或頭皮表面的總體反映。因此,研究睡眠腦電所蘊含的信息以及掌握睡眠周期的變化規(guī)律,對診斷和治療與睡眠相關(guān)的疾病有著重大意義。 睡眠腦電信號是一種非線性、非平穩(wěn)信號,在不同時刻有不同的頻率。按頻率分布情況,腦電信號主要包含著四種不同頻率的波,即δ節(jié)律波(1～4Hz)、θ節(jié)律波(4-8Hz)、α節(jié)律波(8-13Hz)、β節(jié)律波(13-30Hz)。腦電信號中節(jié)律波的提取,是研究腦電信號中不可或缺的重要環(huán)節(jié)。小波變換是信號分析的重要工具,是上世紀(jì)80年代后期發(fā)展起來的應(yīng)用數(shù)學(xué)分支,是傅里葉變換的新發(fā)展。小波變換克服了傅里葉變換的局限性,在時域和頻域都有很好的局部化特性。 本文主要介紹了人睡眠的相關(guān)背景知識、腦電信號研究狀況以及腦電信號的特點；詳細介紹了小波理論方法、小波變換在腦電信號中的去噪應(yīng)用、小波理論在睡眠腦電節(jié)律提取中的應(yīng)用；簡單闡述了睡眠分期準(zhǔn)則、睡眠各期腦電特征以及介紹了小波包能量譜在睡眠分期中的應(yīng)用研究。實驗表明,小波理論的應(yīng)用能夠明顯去除腦電信號中噪聲信號,且保留了原始信號的重要信息；同時,應(yīng)用小波分解及小波包分解都能夠有效地提取腦電信號中各種節(jié)律波；最后,基于小波包能量譜提取的特征向量,統(tǒng)計其數(shù)學(xué)規(guī)律,發(fā)現(xiàn)能夠區(qū)分睡眠各期,為睡眠分期提供了一個重要特征參數(shù)。 醫(yī)學(xué)學(xué)者已發(fā)現(xiàn)人在入睡和深睡階段主要是腦電信號中的δ節(jié)律波和0節(jié)律波起著重要作用,因此本文最后提出一種基于腦電6和0節(jié)律波的腦電生物反饋療法來進行對失眠患者的治療。
[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, 偽 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.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7;O174.2

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