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

Sleep. 1996 Jan;19(1):26-35. doi: 10.1093/sleep/19.1.26.

Abstract

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.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Benzodiazepines / therapeutic use*
  • Depressive Disorder / drug therapy*
  • Depressive Disorder / psychology
  • Electroencephalography
  • Electronic Data Processing
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Observer Variation*
  • Polysomnography
  • Sleep Initiation and Maintenance Disorders / drug therapy*
  • Sleep Stages
  • Sleep, REM

Substances

  • Benzodiazepines