北京生物醫(yī)學(xué)工程

基于腦電時-空特征的深度學(xué)習(xí)失眠障礙檢測算法

Deep learning insomnia disorder detection algorithm based on EEG time-space characteristics

作者：范藝晶羅濤李劍峰楊子賢

單位：北京郵電大學(xué)（北京 100876） 通信作者：羅濤。E-mail：[email protected]

關(guān)鍵詞：腦電信號；失眠障礙；卷積神經(jīng)網(wǎng)絡(luò)；長短期記憶網(wǎng)絡(luò)；雙向長短期記憶網(wǎng)絡(luò)

分類號：R318.04

出版年·卷·期（頁碼）：2022·41·2（161-166）

摘要：

目的現(xiàn)有失眠障礙檢測算法一般包括睡眠分期和失眠障礙識別兩個階段，存在差錯傳播問題，且計算量大。基于此，論文提出一種基于CNN-BiLSTM的深度學(xué)習(xí)算法，直接檢測失眠障礙。方法首先結(jié)合睡眠腦電信號時空模式，根據(jù)電極分布構(gòu)造特征矩陣，再通過CNN表達(dá)其高級特征。隨后饋送至BiLSTM中挖掘睡眠階段間的時序信息，實現(xiàn)失眠障礙的直接檢測。最后按照6:2:2的比例隨機(jī)設(shè)置訓(xùn)練集、驗證集、測試集，用準(zhǔn)確率作為指標(biāo)評估算法模型的分類效果。結(jié)果在ISRUC-Sleep公開數(shù)據(jù)集上進(jìn)行實驗，測試集準(zhǔn)確率為93.25%，可達(dá)到兩階段方法的準(zhǔn)確率水平。結(jié)論本文設(shè)計的CNN-BiLSTM算法模型能夠有效檢測失眠障礙，將為輔助醫(yī)生高效地診斷失眠障礙提供可靠技術(shù)方法。

Objective The existing insomnia detection algorithms generally include two stages: sleep staging and insomnia recognition, which have the problem of error propagation and mass computing. Based on this, this paper proposes a CNN-BiLSTM algorithm for direct insomnia detection. Methods Based on the spatiotemporal pattern of sleep EEG signal, we first construct the feature matrix according to the electrode distribution and use CNN to extract advanced features. Then the feature vector is input into BiLSTM to extract time sequence information. So as to realize the direct insomnia detection. Finally, we randomly set the training set, validation set and test set according to the ratio of 6:2:2. The accuracy is used to evaluate the effect of the algorithm model. Results Experiments accuracy on ISRUC-Sleep public dataset is 93.25%, which can reach the accuracy level of two-stage method. Conclusions The CNN-BiLSTM algorithm in this paper can effectively detect insomnia, which will provide a reliable technical method for doctors to diagnose insomnia.

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