Browse by author
Lookup NU author(s): Jennifer Caffarel, Emeritus Professor John Gibson, Dr Clive Griffiths, Dr Michael DrinnanORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
We have compared sleep staging by an automated neural network (ANN) system, BioSleep (TM) (Oxford BioSignals) and a human scorer using the Rechtschaffen and Kales scoring system. Sleep study recordings from 114 patients with suspected obstructed sleep apnoea syndrome (OSA) were analysed by ANN and by a blinded human scorer. We also examined human scorer reliability by calculating the agreement between the index scorer and a second independent blinded scorer for 28 of the 114 studies. For each study, we built contingency tables on an epoch-by-epoch (30 s epochs) comparison basis. From these, we derived kappa (kappa) coefficients for different combinations of sleep stages. The overall agreement of automatic and manual scoring for the 114 studies for the classification {wake vertical bar light-sleep vertical bar deep-sleep vertical bar REM} was poor (median kappa = 0.305) and only a little better (kappa = 0.449) for the crude {wake vertical bar sleep} distinction. For the subgroup of 28 randomly selected studies, the overall agreement of automatic and manual scoring was again relatively low (j = 0.331 for {wake vertical bar light-sleep vertical bar deep-sleep vertical bar REM} and kappa = 0.505 for {wake vertical bar sleep}), whereas inter-scorer reliability was higher (kappa = 0.641 for {wake vertical bar light-sleep vertical bar deep-sleep vertical bar REM} and kappa = 0.737 for {wake vertical bar sleep}). We conclude that such an ANN-based analysis system is not sufficiently accurate for sleep study analyses using the R&K classification system.
Author(s): Caffarel J, Gibson GJ, Harrison JP, Griffiths CJ, Drinnan MJ
Publication type: Article
Publication status: Published
Journal: Medical and Biological Engineering and Computing
Year: 2006
Volume: 44
Issue: 1-2
Pages: 105-110
ISSN (print): 0140-0118
ISSN (electronic): 1741-0444
Publisher: Springer
URL: http://dx.doi.org/10.1007/s11517-005-0002-4
DOI: 10.1007/s11517-005-0002-4
Altmetrics provided by Altmetric
