Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients

• Published in: Sleep (New York, N.Y.) N.Y.), 1996-01, Vol.19 (1), p.26-35
• Main Authors: Schaltenbrand, N, Lengelle, R, Toussaint, M, Luthringer, R, Carelli, G, Jacqmin, A, Lainey, E, Muzet, A, Macher, J P
• Format: Article
• Language: English
• Subjects: Adult; Aged; Benzodiazepines - therapeutic use; Bioengineering; Depressive Disorder - drug therapy; Depressive Disorder - psychology; Electroencephalography; Electronic Data Processing; Female; Humans; Life Sciences; Male; Middle Aged; Neural Networks (Computer); Observer Variation; Polysomnography; Sleep Initiation and Maintenance Disorders - drug therapy; Sleep Stages; Sleep, REM
• ISSN: 0161-8105; 1550-9109
• DOI: 10.1093/sleep/19.1.26

In this paper, we compare and analyze the results from automatic analysis and visual scoring of nocturnal sleep recordings. The validation is based on a sleep recording set of 60 subjects (33 males and 27 females), consisting of three groups: 20 normal controls subjects, 20 depressed patients and 20 insomniac patients treated with a benzodiazepine. The inter-expert variability estimated from these 60 recordings (61,949 epochs) indicated an average agreement rate of 87.5% between two experts on the basis of 30-second epochs. The automatic scoring system, compared in the same way with one expert, achieved an average agreement rate of 82.3%, without expert supervision. By adding expert supervision for ambiguous and unknown epochs, detected by computation of an uncertainty index and unknown rejection, the automatic/expert agreement grew from 82.3% to 90%, with supervision over only 20% of the night. Bearing in mind the composition and the size of the test sample, the automated sleep staging system achieved a satisfactory performance level and may be considered a useful alternative to visual sleep stage scoring for large-scale investigations of human sleep.