A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal

作者：

Highlights：

• A novel time domain feature is proposed for design an automatic sleep scoring system.

• The method employs a new symbolic idea to explore hidden dynamics of EEG sleep stages.

• The effectiveness of the features is validated by statistical and graphical analysis.

• Compared with other existing methods, our method provides robust and superior results.

摘要

•A novel time domain feature is proposed for design an automatic sleep scoring system.•The method employs a new symbolic idea to explore hidden dynamics of EEG sleep stages.•The effectiveness of the features is validated by statistical and graphical analysis.•Compared with other existing methods, our method provides robust and superior results.

论文关键词：Electroencephalography (EEG),Sleep stage classification,Symbolic analysis,Statistical Behavior of Local Extrema (SBLE),Multi-Cluster/Class Feature Selection (MCFS),Support Vector Machine (SVM)

论文评审过程：Received 24 August 2017, Revised 12 March 2018, Accepted 13 March 2018, Available online 14 March 2018, Version of Record 4 April 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.03.020