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Sleep Stage Scoring Using the Neural Network Model: Comparison Between Visual and Automatic Analysis in Normal Subjects and Patients

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 control 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.
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https://hal-utt.archives-ouvertes.fr/hal-02362313
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Wednesday, November 13, 2019 - 6:26:55 PM
Last modification on : Thursday, November 14, 2019 - 1:36:43 AM

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N. Schaltenbrand, Régis Lengellé, M. Toussaint, R. Luthringer, G. Carelli, et al.. Sleep Stage Scoring Using the Neural Network Model: Comparison Between Visual and Automatic Analysis in Normal Subjects and Patients. SLEEP, American Academy of Sleep Medicine, 1996, 19 (1), pp.26-35. ⟨10.1093/sleep/19.1.26⟩. ⟨hal-02362313⟩

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