Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

Abstract

Study Objectives: To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders. Methods: Sleep recordings were included of 95 patients (38 women, 40.5 ± 13.7 years) diagnosed with insomnia (n = 23), idiopathic hypersomnia (n = 24), narcolepsy (n = 24), and obstructive sleep apnea (n = 24). Visual staging (VS) was performed by two experts (VS1 and VS2) according to the American Academy of Sleep Medicine rules. AS was based on the analysis of a single electroencephalogram channel (Cz-Pz), without any information from electro-oculography nor electromyography. The epoch-by-epoch agreement (concordance and Conger's coefficient [κ]) was compared pairwise (VS1-VS2, AS-VS1, AS-VS2) and between AS and consensual VS. Sleep parameters were also compared. Results: The pairwise agreements were: between AS and VS1, 78.6% (κ = 0.70); AS and VS2, 75.0% (0.65); and VS1 and VS2, 79.5% (0.72). Agreement between AS and consensual VS was 85.6% (0.80), with the following distribution: insomnia 85.5% (0.80), narcolepsy 83.8% (0.78), idiopathic hypersomnia 86.1% (0.68), and obstructive sleep disorder 87.2% (0.82). A significant low-amplitude scorer effect was observed for most sleep parameters, not always driven by the same scorer. Hypnograms obtained with AS and VS exhibited very close sleep organization, except for 80% of rapid eye movement sleep onset in the group diagnosed with narcolepsy missed by AS. Conclusions: Agreement between AS and VS in sleep disorders is comparable to that reported in healthy individuals and to interexpert agreement in patients. ASEEGA could therefore be considered as a complementary sleep stage scoring tool in clinical practice, after improvement of rapid eye movement sleep onset detection.

Heterogeneity in the links between sleep arousals, amyloid-β, and cognition

“…Sleep stage scoring and arousal detection were carried out in separate steps by 2 independent algorithms. Sleep stage scoring was performed in 30-second windows using a validated algorithm (ASEEGA, Physip) ( 33 , 34 ). Automatic arousal detection was then computed as it is objective and reproducible, and because it saves time ( 35 ).…”

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

“…The accuracy of the models mostly ranges from 78-90%. [8][9][10][11][12] Development and evaluation of software dedicated to automatic sleep staging (AS) face several issues, among which are: (1) EEG has a low signal-to-noise ratio (SNR), as the brain activity measured is often covered by multiple sources of environmental, physiological, and activity-specific noise called "artifacts"; (2) the generalization capabilities of models need to be further verified, for patients of different ages, pathophysiological, and treatment in the real world. 13,14 Behavioral and physiological characteristics of sleep in normal children vary significantly from sleep in adults.…”

Variability of sleep stage scoring in late midlife and early old age

“…Yet, investigation of this likely phenomenon remains insufficient. Hence, we investigated inter‐expert variability at a relatively early stage of the aging process, by comparing three sleep stage scorings, two performed by visual experts from different centres (EXPERT_1 and EXPERT_2) without prior alignment between them, and automatic scoring (ALGO) using a previously validated stage scoring algorithm (Berthomier et al, 2007; Peter‐Derex et al, 2020).…”