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DOI 10.1007/s11517-005-0002-4

Медицинская библиотека · Medical & biological engineering & computing · 2006

Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.

Caffarel Jennifer, Gibson G John, Harrison J Phil, Griffiths Clive J, Drinnan Michael J

Сравнительное исследованиеСтатьяПеревод пока не подготовлен

Аннотация

We have compared sleep staging by an automated neural network (ANN) system, BioSleep (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 / light-sleep / deep-sleep / REM} was poor (median kappa = 0.305) and only a little better (kappa = 0.449) for the crude {wake / sleep} distinction. For the subgroup of 28 randomly selected studies, the overall agreement of automatic and manual scoring was again relatively low (kappa = 0.331 for {wake light-sleep / deep-sleep REM} and kappa = 0.505 for {wake / sleep}), whereas inter-scorer reliability was higher (kappa = -0.641 for {wake / light-sleep / deep-sleep / REM} and kappa = 0.737 for {wake / sleep}). We conclude that such an ANN-based analysis system is not sufficiently accurate for sleep study analyses using the R&K classification system.

Для цитирования: Caffarel Jennifer, Gibson G John, Harrison J Phil, Griffiths Clive J, Drinnan Michael J. Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.. Medical & biological engineering & computing. 2006. DOI: 10.1007/s11517-005-0002-4.

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