基于单通道脑电信号的自动睡眠分期模型研究

Automatic sleep staging model based on single channel electroencephalogram signal

睡眠分期是解决睡眠问题的基础。针对现阶段单通道脑电(EEG)数据和特征决定自动睡眠分期模型分类精度的上限问题,本文提出一种将深度卷积神经网络(DCNN)和双向长短期记忆神经网络(BiLSTM)混合的自动睡眠分期模型。模型使用DCNN自动学习EEG信号的时频域特征,使用BiLSTM提取数据之间的时序特征,充分挖掘数据包含的特征信息,以提高自动睡眠分期的准确率。同时,使用降噪技术与自适应合成采样技术减少信号噪声和不平衡数据集对模型性能的影响。本文采用欧洲数据格式存储的睡眠数据集拓展版和上海精神卫生中心收集的睡眠数据集进行实验,分别取得了86.9%和88.9%的整体准确率。与基础网络模型进行对比分析,实验结果均优于基础网络,进一步证明了本文模型的有效性,可为构建基于单通道EEG信号的家庭睡眠监测系统提供借鉴。

Sleep staging is the basis for solving sleep problems.There’s an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram(EEG)data and features.To address this problem,this paper proposed an automatic sleep staging model that mixes deep convolutional neural network(DCNN)and bi-directional long short-term memory network(BiLSTM).The model used DCNN to automatically learn the timefrequency domain features of EEG signals,and used BiLSTM to extract the temporal features between the data,fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging.At the same time,noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance.In this paper,experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database,and achieved an overall accuracy rate of 86.9%and 88.9%respectively.When compared with the basic network model,all the experimental results outperformed the basic network,further demonstrating the validity of this paper's model,which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.