DocumentCode
636894
Title
Automatic detection of the wake and stage 1 sleep stages using the EEG sub-epoch approach
Author
Malaekah, Emad ; Cvetkovic, Dean
Author_Institution
Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia
fYear
2013
fDate
3-7 July 2013
Firstpage
6401
Lastpage
6404
Abstract
Studies by Rechtschaffen and Kales (R&K), rely on 30-sec epochs to score sleep stages. In this paper, we introduce a new approach based on three consecutive and non-consecutive 6-sec sub-epochs for the detection of the wake stage and stage 1 sleep. The Relative Spectral Energy Band (RSEB) is used as a feature extraction from the electroencephalographic (EEG) signal. Spectral estimation is performed using non-parametric and parametric methods. We then compared the performance of the conventional 30-sec epochs with the three consecutive and non-consecutive 6-sec epochs. The outcomes of this study showed that while the accuracy varies between subjects, the non-parametric method proved to be more effective with stage 1 sleep detection and the parametric method was more effective for wake stage detection. The non-consecutive sub-epoch method was more effective and consecutive method was least effective in non-parametric stage 1 detection. Alternatively, the 30-second epoch method was most effective for parametric wake stage detection.
Keywords
bioelectric potentials; electroencephalography; feature extraction; medical signal detection; medical signal processing; neurophysiology; sleep; electroencephalographic signal; feature extraction; parametric wake stage detection; relative spectral energy band; sleep stage detection; spectral estimation; the EEG sub-epoch approach; Accuracy; Educational institutions; Electroencephalography; Feature extraction; Filtering; Manuals; Sleep;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location
Osaka
ISSN
1557-170X
Type
conf
DOI
10.1109/EMBC.2013.6611019
Filename
6611019
Link To Document