本文選題：睡眠分期 + 腦電　； 參考：《計(jì)算機(jī)工程》2017年10期

【摘要】：為實(shí)現(xiàn)準(zhǔn)確的自動(dòng)睡眠分期,且滿(mǎn)足泛化能力的需求,基于腦電(EEG)和肌電(EMG)多特征,提出一種自動(dòng)睡眠分期方法。以MIT-BIH多導(dǎo)睡眠數(shù)據(jù)庫(kù)中樣本的EEG和EMG為分析對(duì)象,采用離散小波變換對(duì)原始數(shù)據(jù)進(jìn)行濾波預(yù)處理,提取EEG的α,β,θ,δ節(jié)律波和高頻成分的能量比,利用樣本熵算法提取EEG的非線(xiàn)性特征。將特征參數(shù)輸入支持向量機(jī)分類(lèi)器中進(jìn)行樣本訓(xùn)練與分類(lèi)識(shí)別。實(shí)驗(yàn)結(jié)果表明,該方法的分期準(zhǔn)確率可以達(dá)到92.94%,相比基于EEG的睡眠分期方法平均準(zhǔn)確率提高3.96%,交叉驗(yàn)證平均準(zhǔn)確率達(dá)82.68%,具有較好的泛化能力。
[Abstract]:In order to realize accurate automatic sleep staging and meet the requirement of generalization ability, an automatic sleep staging method was proposed based on the features of EEG and EMG. The EEG and EMG of the samples in MIT-BIH polysomnography database were analyzed. The original data were filtered by discrete wavelet transform, and the energy ratios of 偽, 尾, 胃, 未 rhythm waves and high frequency components of EEG were extracted. The nonlinear feature of EEG is extracted by sample entropy algorithm. Input feature parameters into support vector machine classifier for sample training and classification recognition. The experimental results show that the accuracy of this method can reach 92.940.Compared with the sleep staging method based on EEG, the average accuracy is increased 3.96, and the average accuracy of cross-validation is 82.68, which has better generalization ability.
【作者單位】： 上海大學(xué)機(jī)電工程與自動(dòng)化學(xué)院;中國(guó)科學(xué)院蘇州生物醫(yī)學(xué)工程技術(shù)研究所;
【基金】：國(guó)家自然科學(xué)基金(61433016) 蘇州市科技計(jì)劃項(xiàng)目(ZXY201427,ZXY201429)
【分類(lèi)號(hào)】：R740;TN911.6

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本文編號(hào)：2022136