基于MSE-PCA的脑电睡眠分期方法研究
|
摘要
针对传统的自动睡眠分期准确率不足问题,提出一种将多尺度熵(MSE)和主成分分析(PCA)联合使用的自动睡眠分期方法。以8例受试者睡眠脑电(EEG)监测数据及专家人工分期结果作为样本,首先使用MSE表征受试者脑电信号不同睡眠期的非线性动力学特征;然后使用PCA的前两个主成分向量代替MSE特征进行降维,实现降低数据冗余的同时保留绝大多数EEG非线性特征;最终将新向量的特征参数输入到反馈神经网络(BPNN)分类器中实现MSE-PCA模型的脑电睡眠状态的自动识别分类。实验结果表明,自动分期准确率可达到87.9%,kappa系数0.77,该方法能提高脑电自动睡眠分期系统的准确率和稳定性。
Aiming at the problem of insufficient accuracy of traditional automatic sleep staging, a new method of automatic sleep staging based on a fusion algorithm, multi-scale entropy( MSE) and principal component analysis( PCA), is proposed. In this work,the data of sleep EEG monitoring and the expert staging of 8 subjects are utilized as samples. Firstly, MSE is used to extract the nonlinear dynamic features from sleep stages. Then this features are replaced by the first two principal component vectors of PCA.The purpose is reduce the data dimension redundancy, as well as retaining the vast majority of EEG non-linear features. After that the new vector are entered into the BPNN classifier to implement the MSE-PCA model of automatic sleep staging. The experimental results show that the accuracy of automatic staging can reach to 87. 9 % and kappa coefficient is 0. 77, which can improve the accuracy and stability of automatic EEG sleep staging system.
Aiming at the problem of insufficient accuracy of traditional automatic sleep staging, a new method of automatic sleep staging based on a fusion algorithm, multi-scale entropy( MSE) and principal component analysis( PCA), is proposed. In this work,the data of sleep EEG monitoring and the expert staging of 8 subjects are utilized as samples. Firstly, MSE is used to extract the nonlinear dynamic features from sleep stages. Then this features are replaced by the first two principal component vectors of PCA.The purpose is reduce the data dimension redundancy, as well as retaining the vast majority of EEG non-linear features. After that the new vector are entered into the BPNN classifier to implement the MSE-PCA model of automatic sleep staging. The experimental results show that the accuracy of automatic staging can reach to 87. 9 % and kappa coefficient is 0. 77, which can improve the accuracy and stability of automatic EEG sleep staging system.
引文
[1]高群霞,周静,吴效明.基于脑电信号的自动睡眠分期研究进展[J].生物医学工程学杂志,2015,32(05):1155-1159.
[2]AARON R S,LIANG C K.A study on sleep EEG using sample entropy and power spectrum analysis[C].Defense Science Research Conference and Expo(DSR),Singapore,2011:1-4.
[3]GUO C,LU F,LIU S,et al.Sleep EEG staging based on Hilbert-Huang Transform and Sample Entropy[C].International Conference on Computational Intelligence and Communication Networks(CICN),Jabalpur,2015:442-445.
[4]谢宏,施小南.基于离散小波变换的脑电信号睡眠分期研究[J].微型机与应用,2015,34(432):18-20.
[5]KEMP B.The sleep-edf database online.[Online].Available:http://www.physionet.org/physiobank/database/sleep-edf/
[6]STOCHHOLM A,MIKKELSEN K,KIDMOSE P.Automatic sleep stage classification using EEG[C].Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC),Orlando,2016:4751-4754.
[7]FRAIWAN L,LWEESY K.Newborn sleep stage identification using multi-scale entropy[C].IEEE 2nd Middle East Conference on Biomedical Engineering,Doha,2014:361-364.
[8]LIANG S F,KUO C E,HU Y H,et al.Automatic stage scoring of single-channel sleep EEG by using multi-scale entropy and autoregressive models[J].IEEE Transactions on Instrumentation and Measurement,2012,42(13):1649-1657.
[9]周鹏,李向新,张翼,等.基于主成分分析和支持向量机的睡眠分期研究[J].生物医学工程学杂志,2013,30(6):1176-1179.
[10]杨静,王成,谢成颖,等.基于主成分分析和反向传播神经网络的肝癌细胞后向散射显微光谱判别[J].生物医学工程学杂志,2017,34(2):246-252.
[11]王金甲,周丽娜.基于PCA和LDA数据降维的脑磁图脑机接口研究[J].生物医学工程学杂志,2011(6):1069-1074.
[2]AARON R S,LIANG C K.A study on sleep EEG using sample entropy and power spectrum analysis[C].Defense Science Research Conference and Expo(DSR),Singapore,2011:1-4.
[3]GUO C,LU F,LIU S,et al.Sleep EEG staging based on Hilbert-Huang Transform and Sample Entropy[C].International Conference on Computational Intelligence and Communication Networks(CICN),Jabalpur,2015:442-445.
[4]谢宏,施小南.基于离散小波变换的脑电信号睡眠分期研究[J].微型机与应用,2015,34(432):18-20.
[5]KEMP B.The sleep-edf database online.[Online].Available:http://www.physionet.org/physiobank/database/sleep-edf/
[6]STOCHHOLM A,MIKKELSEN K,KIDMOSE P.Automatic sleep stage classification using EEG[C].Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC),Orlando,2016:4751-4754.
[7]FRAIWAN L,LWEESY K.Newborn sleep stage identification using multi-scale entropy[C].IEEE 2nd Middle East Conference on Biomedical Engineering,Doha,2014:361-364.
[8]LIANG S F,KUO C E,HU Y H,et al.Automatic stage scoring of single-channel sleep EEG by using multi-scale entropy and autoregressive models[J].IEEE Transactions on Instrumentation and Measurement,2012,42(13):1649-1657.
[9]周鹏,李向新,张翼,等.基于主成分分析和支持向量机的睡眠分期研究[J].生物医学工程学杂志,2013,30(6):1176-1179.
[10]杨静,王成,谢成颖,等.基于主成分分析和反向传播神经网络的肝癌细胞后向散射显微光谱判别[J].生物医学工程学杂志,2017,34(2):246-252.
[11]王金甲,周丽娜.基于PCA和LDA数据降维的脑磁图脑机接口研究[J].生物医学工程学杂志,2011(6):1069-1074.