Automated sleep staging using single EEG channel for REM sleep deprivation

Yu Hsun Lee*, Yong-Sheng Chen, Li Fen Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In medical literatures, it has been reported that the increased REM (rapid eye movement) density is one of the characters of depressed sleep. Some experiments were conducted to confirm that REM sleep deprivation (REM-SD) for a period of time is therapeutic for endogenous depressed patients. However, because of its high complexity and intensive labor requirement, this therapy has not yet been proved validity by a sufficient amount of depressed patients. Therefore, we propose to develop an automated sleep staging system using only single EEG channel to achieve on-line detection for REM state during sleep. For classifier design, we use a dataset of 25 subjects and the staging accuracy can achieve 80%. Once the REM state is detected by the system, the system will alarm the subject to deprive the REM sleep. The effect of REM sleep deprivation can be examined by hypnogram and the proposed system will be applied for clinical trials of depression therapy.

Original languageEnglish
Title of host publicationProceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Pages439-442
Number of pages4
DOIs
StatePublished - 18 Nov 2009
Event2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 - Taichung, Taiwan
Duration: 22 Jun 200924 Jun 2009

Publication series

NameProceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009

Conference

Conference2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Country/TerritoryTaiwan
CityTaichung
Period22/06/0924/06/09

Keywords

  • Automated sleep staging
  • Depression
  • REM
  • Sleep deprivation

Fingerprint

Dive into the research topics of 'Automated sleep staging using single EEG channel for REM sleep deprivation'. Together they form a unique fingerprint.

Cite this