Fast and accurate PLS-based classification of EEG sleep using single channel data

作者：

Highlights：

• Fast classification of sleep and wake stages using a single EEG channel is proposed.

• The dataset was provided by Physionet.

• Speed and accuracy of PLS were compared with those of k-NN, Bayes and LDC classifiers.

• Results indicated that the Pz-Cz channel had better accuracy than the Fpz-Cz channel.

• We achieved 91% classification accuracy by selecting PLS as the classifier.

摘要

•Fast classification of sleep and wake stages using a single EEG channel is proposed.•The dataset was provided by Physionet.•Speed and accuracy of PLS were compared with those of k-NN, Bayes and LDC classifiers.•Results indicated that the Pz-Cz channel had better accuracy than the Fpz-Cz channel.•We achieved 91% classification accuracy by selecting PLS as the classifier.

论文关键词：Partial least squares regression,Auto-regressive model,Electroencephalograph,k-Nearest neighborhood,Bayes,Linear discriminant classifier

论文评审过程：Available online 10 June 2015, Version of Record 28 June 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.06.010