本文關(guān)鍵詞： 睡眠腦電 小波變換 腦電節(jié)律 小波能量 睡眠分期　出處：《南京大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：睡眠是高等脊椎動(dòng)物的一種重要的生理現(xiàn)象。在睡眠研究中,腦電圖(EEG)是一個(gè)重要而有力的工具。腦電信號(hào)包含著眾多的生理和病理信息,常用于輔助研究人類大腦活動(dòng)與診治睡眠疾患。本文簡(jiǎn)要介紹了小波變換和小波包分解的原理,用其對(duì)原始睡眠腦電信號(hào)進(jìn)行去噪處理并提取基本特征節(jié)律(δ波,θ波,α波,β波)。在此基礎(chǔ)上,對(duì)睡眠各階段的腦電信號(hào)進(jìn)行小波能量分析。腦電信號(hào)是由腦的神經(jīng)系統(tǒng)產(chǎn)生的電生理反應(yīng)。它與很多其他生理信號(hào)相似,幅值較小,信噪比低,為非平穩(wěn)信號(hào),且容易受到各種外界因素的干擾,如眼電、心電、肌電、白噪聲等。在原始睡眠腦電信號(hào)預(yù)處理中,本文采用了小波包自適應(yīng)閾值去噪的分析方法,在腦電去噪中與小波去噪相比取得了較好的效果,并采用了小波變換提取了腦電基本節(jié)律。睡眠分期的研究主要是對(duì)睡眠腦電信號(hào)中的α波和δ波進(jìn)行分析,計(jì)算睡眠各期腦電節(jié)律α波和δ波的小波能量比值。由于這兩個(gè)頻帶之間的能量比在睡眠各個(gè)階段呈現(xiàn)出明顯差異,并且其變化趨勢(shì)具有一定的規(guī)律性。其值在清醒期最大,NREM I期、Ⅱ期不斷減小,Ⅲ期、Ⅳ期的時(shí)候達(dá)到最低值,進(jìn)入REM期后又回升到接近Ⅰ期值。除REM期和Ⅰ期的值比較接近外,其余各期之間分界比較明顯。與人工分期結(jié)果相比較的實(shí)驗(yàn)結(jié)果表明,睡眠各期腦電節(jié)律α波和δ波的小波能量比值為睡眠分期提供了一種新的特征變量,在睡眠分期的研究中具有一定的實(shí)用價(jià)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, 偽 wave, 尾 wave) is extracted from the denoising of the original sleep EEG signal, and the basic characteristic rhythm (未 wave, 胃 wave, 偽 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 偽 wave and 未 wave in sleep EEG signal. The wavelet energy ratio of 偽 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，

本文編號(hào)：1540947