北京生物醫(yī)學(xué)工程

基于多特征融合的睡眠分期

Sleep staging based on multi-feature fusion

作者：熊馨羅劍花王春武易三莉劉瑞湘賀建峰

單位：昆明理工大學(xué)信息工程與自動(dòng)化學(xué)院（昆明650500） 吉林師范大學(xué)信息與技術(shù)學(xué)院（吉林四平136000） 云南省第二人民醫(yī)院臨床心理科（昆明650021） 通信作者：賀建峰，教授。E-mails: [email protected]

關(guān)鍵詞：睡眠分期；腦電；多特征融合；樣本熵；小波包能量；去趨勢(shì)波動(dòng)

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2021·40·5（487-493）

摘要：

目的為了有效實(shí)現(xiàn)睡眠自動(dòng)分期，對(duì)睡眠障礙等相關(guān)疾病的診斷提供更多依據(jù)，本文提出了一種基于多特征融合的睡眠分期方法。方法數(shù)據(jù)來(lái)自ISRUC-Sleep數(shù)據(jù)庫(kù)，首先對(duì)10名健康受試者和10名睡眠障礙患者的腦電（electroencephalogram ，EEG）信號(hào)計(jì)算3種特征—-樣本熵、小波包能量和去趨勢(shì)波動(dòng)。然后采用支持向量機(jī)（support vector machine, SVM）構(gòu)建睡眠分期模型，并驗(yàn)證該模型的準(zhǔn)確性。此外，為了進(jìn)行比較加入心電（electrocardiogram ，ECG）和肌電（electromyogram ，EMG）通道。結(jié)果健康受試者和睡眠障礙患者睡眠分期的準(zhǔn)確率分別達(dá)到87.4%和86.3%。結(jié)論基于多特征融合的睡眠分期方法能夠有效地提高睡眠分期的準(zhǔn)確率。

Objective In order to realize the automatic sleep staging effectively and to provide more evidence for the diagnosis of sleep disorders and other related diseases, a sleep staging method based on multi-feature fusion is proposed in this paper. Methods Electroencephalogram(EEG) signals come from ISRUC-Sleep database, including 10 healthy subjects and 10 patients with sleep disorder problems. Three characteristics that include sample entropy, wavelet packet energy and detrended fluctuations were calculated. Then Support Vector Machine (SVM) was used to construct the sleep staging model and the accuracy of the model was verified. Additionally, electrocardiogram(ECG) and electromyogram(EMG) signals were also involved for comparison. Results The accuracy of sleep staging in two groups reached 87.4% and 86.3% respectively. Conclusions The sleep staging method based on multi-feature fusion can effectively improve the accuracy of sleep staging.

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