發(fā)布時(shí)間：2018-07-02 08:17

  本文選題：睡眠分期 + 樣本熵　； 參考：《江蘇大學(xué)學(xué)報(bào)(自然科學(xué)版)》2009年05期

【摘要】：運(yùn)用樣本熵從波士頓Beth Israel睡眠腦電實(shí)驗(yàn)數(shù)據(jù)中提取睡眠特征值,對(duì)睡眠分期進(jìn)行研究.針對(duì)腦電屬于微弱非平穩(wěn)隨機(jī)信號(hào)、難于提取特征的特點(diǎn),利用小波變換先有效地消除腦電信號(hào)中的噪聲,再計(jì)算其樣本熵用以表征睡眠各分期.計(jì)算結(jié)果表明,由清醒期到非快速眼動(dòng)的Ⅳ期過(guò)程中,其樣本熵值呈規(guī)律性逐漸變小,與該庫(kù)中專(zhuān)家評(píng)定的結(jié)果相符.這說(shuō)明經(jīng)過(guò)小波消噪和樣本熵處理的腦電信號(hào)能準(zhǔn)確地反映睡眠各期的變化特征,比用近似熵表征睡眠分期更準(zhǔn)確、運(yùn)算速度更快,完全適用于非平穩(wěn)隨機(jī)信號(hào)的處理.
[Abstract]:The sleep stages were studied by extracting the sleep characteristic values from the data of Beth Israel sleep EEG experiment in Boston using sample entropy. Because EEG is a weak non-stationary random signal, it is difficult to extract features. Wavelet transform is used to effectively eliminate the noise in EEG signal, and then the sample entropy is calculated to characterize the sleep stages. The calculated results show that the entropy values of the samples decrease gradually during the period 鈪，

本文編號(hào)：2089500