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浙江大学学报(工学版)  2020, Vol. 54 Issue (10): 1899-1905    DOI: 10.3785/j.issn.1008-973X.2020.10.005
计算机技术     
基于深度卷积神经网络的睡眠分期模型
贾子钰1,2(),林友芳1,2,3,张宏钧1,王晶1,2,3,*()
1. 北京交通大学 计算机与信息技术学院,北京 100044
2. 北京交通大学 交通数据分析与挖掘北京市重点实验室,北京 100044
3. 中国民用航空局 民航旅客服务智能化应用技术重点实验室,北京 100105
Sleep stage classification model based ondeep convolutional neural network
Zi-yu JIA1,2(),You-fang LIN1,2,3,Hong-jun ZHANG1,Jing WANG1,2,3,*()
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
3. Key Laboratory of Intelligent Passenger Service of Civil Aviation, CAAC, Beijing 100105, China
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摘要:

针对现阶段数据和特征决定睡眠分期模型的分类精度上限的问题,提出深度卷积神经网络模型. 在模型主体构建方面,并行卷积网络可以自动学习原始信号的时域特征和频域特征,特征融合网络通过空洞卷积和残差连接进行多特征融合,分类网络基于融合后的特征进行睡眠分期. 利用生成少数类过采样技术(SMOTE)减少类别不平衡对分类效果的影响,结合两步训练法对模型进行优化. 实验使用Sleep-EDF数据集的原始单导脑电信号(Fpz-Cz通道)对模型进行20折交叉验证,得到总体精度和宏F1分别为86.73%和81.70%. 提出的深度卷积模型在没有任何先验知识的情况下,对脑电信号进行端到端的学习,分类准确率优于传统的深度学习模型.

关键词: 睡眠分期脑电信号深度学习卷积神经网络(CNN)    
Abstract:

A deep convolutional neural network model was proposed aiming at the problem that the current data and features determine the upper limit of the classification accuracy of the sleep staging model. The parallel convolutional neural network automatically learns the time-domain and frequency-domain features of the original signals in terms of model construction. The feature fusion neural network fuses multi-features through dilated convolution and residual connection. The classification neural network recognizes the sleep stages based on fused features. Synthetic minority oversampling technique (SMOTE) method was applied to enhance data in order to reduce the effect of classification imbalance on classification effect, and two-step training method was applied to optimize the model. The original single-channel electroencephalogram (Fpz-Cz channel) of the Sleep-EDF data set was used to evaluate the proposed model by the 20-fold cross-validate scheme. The overall accuracy and macro-averaging F1-score were 86.73% and 81.70% respectively. The proposed deep convolution neural network was an end-to-end deep learning model without any prior knowledge. The experimental results showed that the classification accuracy of the proposed model was better than traditional deep learning models.

Key words: sleep stage classification    electroencephalogram    deep learning    convolutional neural network (CNN)
收稿日期: 2019-09-10 出版日期: 2020-10-28
CLC:  TP 391  
基金资助: 中央高校基本科研业务费专项资金资助项目(2018YJS039);国家自然科学基金资助项目(61603029)
通讯作者: 王晶     E-mail: ziyujia@bjtu.edu.cn;wj@bjtu.edu.cn
作者简介: 贾子钰(1993—),男,博士生,从事脑机接口与深度学习的研究. orcid.org/0000-0002-8523-1419. E-mail: ziyujia@bjtu.edu.cn
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引用本文:

贾子钰,林友芳,张宏钧,王晶. 基于深度卷积神经网络的睡眠分期模型[J]. 浙江大学学报(工学版), 2020, 54(10): 1899-1905.

Zi-yu JIA,You-fang LIN,Hong-jun ZHANG,Jing WANG. Sleep stage classification model based ondeep convolutional neural network. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1899-1905.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.10.005        http://www.zjujournals.com/eng/CN/Y2020/V54/I10/1899

图 1  深度卷积神经网络架构图
图 2  空洞卷积神经网络
图 3  模型两步训练流程图
指标 数量 所占比例/%
W 7927 18.90
N1 2804 6.68
N2 17799 42.43
N3 5703 13.59
REM 7717 18.40
表 1  Sleep-EDF睡眠分期情况
层名称 层类型 单元数 激活函数 大小 步长
Input1 ? ? ? ? ?
Con11 Convolution 64 Relu 50 6
MaxP11 MaxPooling ? ? 8 8
D11 Dropout (0.5) ? ? ? ?
Con12 Convolution 128 Relu 8 1
Con13 Convolution 128 Relu 8 1
Con14 Convolution 128 Relu 8 1
MaxP12 MaxPooling ? ? 4 4
F1 Flatten ? ? ? ?
Con21 Convolution 64 Relu 400 50
MaxP21 MaxPooling ? ? 4 4
D21 Dropout (0.5) ? ? ? ?
Con22 Convolution 128 Relu 6 1
Con23 Convolution 128 Relu 6 1
Con24 Convolution 128 Relu 6 1
MaxP22 MaxPooling ? ? 2 2
F2 Flatten ? ? ? ?
D3 Dropout (0.5) ? ? ? ?
Dense1 Dense 5 Softmax ? ?
表 2  预训练模型的最佳结构及详细参数
分期 W N1 N2 N3 REM PR/% RE/% F1/%
W 7295 271 131 37 193 88.67 92.03 90.32
N1 369 1396 606 16 417 65.57 49.79 56.60
N2 378 283 15582 853 703 91.21 87.54 89.34
N3 33 3 270 5397 0 85.53 94.63 89.85
REM 152 176 495 7 6887 83.99 89.24 86.54
表 3  基于深度卷积神经网络模型的混淆矩阵及各类评价指标
图 4  睡眠专家人工分期与模型自动睡眠分期对比图
%
模型 ACC MF1 F1
W N1 N2 N3 REM
文献[9]模型 78.9 73.7 71.6 47.0 84.6 84.0 81.4
文献[11]模型 74.8 69.8 65.4 43.7 80.6 84.9 74.5
文献[12]模型 82.0 76.9 84.7 46.6 85.9 84.8 82.4
文献[13]模型 84.3 79.7 89.2 52.2 86.8 85.1 85.0
提出模型 87.1 82.5 90.3 56.6 89.3 89.9 86.5
表 4  先进研究与提出模型的结果对比
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