Complex networks approach for EEG signal sleep stages classification

作者：

Highlights：

• Developing a new EEG sleep stages classification method based on the statistical features in time domain and complex networks properties.

• The method provides better EEG sleep signals classification compared with the existing approaches reported.

• Finding of not all sleep stages can be classified with the same number of the statistical features.

• Stage Awake can be classified with a fewer statistical features due to including high activity compared with other sleep stages.

摘要

•Developing a new EEG sleep stages classification method based on the statistical features in time domain and complex networks properties.•The method provides better EEG sleep signals classification compared with the existing approaches reported.•Finding of not all sleep stages can be classified with the same number of the statistical features.•Stage Awake can be classified with a fewer statistical features due to including high activity compared with other sleep stages.

论文关键词：Electroencephalography,Complex networks,Sleep stages,Statistical features

论文评审过程：Received 11 March 2016, Revised 28 June 2016, Accepted 1 July 2016, Available online 5 July 2016, Version of Record 15 July 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.07.004