本文選題：睡眠分期　切入點(diǎn)：模糊特征　出處：《計(jì)算機(jī)工程與應(yīng)用》2017年23期

【摘要】：利用腦電信號(hào)模糊特征分類的方法對(duì)睡眠進(jìn)行分期研究。首先對(duì)腦電信號(hào)進(jìn)行預(yù)處理,濾除干擾噪聲后使用模糊熵算法、多尺度熵算法以及復(fù)雜度算法對(duì)腦電信號(hào)進(jìn)行特征參數(shù)提取,采用最小二乘支持向量機(jī)(the Least Squares Support Vector Machine,LS-SVM)對(duì)特征參數(shù)進(jìn)行分類,并將睡眠過程分為清醒期、淺睡期、深睡期和快速眼動(dòng)期(Rapid Eye Movement,REM),獲得分期正確率。最后通過上述方法對(duì)2 000組睡眠腦電樣本進(jìn)行睡眠分期測(cè)試,與專家人工分期結(jié)果進(jìn)行比對(duì),將復(fù)雜度輸入到最小二乘支持向量機(jī)進(jìn)行分類的平均正確率是92.65%,高于模糊熵和多尺度熵作為最小二乘向量機(jī)的輸入時(shí)的準(zhǔn)確率�；谀：卣鞯膹�(fù)雜度提取的特征參數(shù)可以作為睡眠分期的有效依據(jù),在保證準(zhǔn)確度的前提下,降低人工成本。
[Abstract]:The stage of sleep was studied by using the fuzzy feature classification of EEG signals.Firstly, the EEG signal is preprocessed, and the fuzzy entropy algorithm, the multi-scale entropy algorithm and the complexity algorithm are used to extract the characteristic parameters of the EEG signal after filtering out the interference noise.The Least Squares Support Vector machine (LS-SVM) was used to classify the characteristic parameters, and the sleep process was divided into waking, shallow, deep and rapid Eye motion stages.Finally, 2000 groups of sleep EEG samples were tested by the above methods, and the results were compared with the results of expert artificial staging.The average accuracy of input complexity into least squares support vector machine is 92.65, which is higher than that of fuzzy entropy and multi-scale entropy as input of least squares vector machine.The feature parameters extracted based on the complexity of fuzzy features can be used as an effective basis for sleep stages and reduce the labor cost under the premise of ensuring accuracy.
【作者單位】： 吉林大學(xué)儀器科學(xué)與電氣工程學(xué)院;
【基金】：國家“十二五”科技支撐計(jì)劃課題(No.2015BAI02B04) 吉林市科技計(jì)劃項(xiàng)目(No.2015313013)
【分類號(hào)】：R740;TN911.7

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本文編號(hào)：1717947