A decision support system for automated identification of sleep stages from single-channel EEG signals

作者：

Highlights：

• A single channel EEG based automated sleep scoring method is proposed.

• A novel signal processing technique, namely TQWT is employed for sleep staging.

• We introduce bagging to classify sleep stages.

• Efficacy of the method is confirmed by statistical and graphical analyses.

• The performance of the proposed scheme, compared to the existing ones is promising.

摘要

•A single channel EEG based automated sleep scoring method is proposed.•A novel signal processing technique, namely TQWT is employed for sleep staging.•We introduce bagging to classify sleep stages.•Efficacy of the method is confirmed by statistical and graphical analyses.•The performance of the proposed scheme, compared to the existing ones is promising.

论文关键词：Sleep,EEG,Classification,Wavelet,Statistical moments

论文评审过程：Received 23 December 2016, Revised 5 May 2017, Accepted 6 May 2017, Available online 8 May 2017, Version of Record 25 May 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.05.005