Thomas Andrillon1, Geoffroy Solelhac2, Paul Bouchequet2, Francesco Romano2, Max-Pol Le Brun3, Marco Brigham3, Mounir Chennaoui4, Damien Léger2. 1. Université de Paris, Equipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, Paris, France; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia. Electronic address: thomas.andrillon@monash.edu. 2. Université de Paris, Equipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, Paris, France; Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, France. 3. Ecole Polytechnique, Centre de mathématiques appliquées (CMAP), Palaiseau, France. 4. Université de Paris, Equipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, Paris, France; Institut de recherche biomédicale des armées (IRBA), Brétigny-sur-Orge, France.
Abstract
BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). METHOD: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. RESULTS: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohen's κ = 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (κ=0.004). CONCLUSION: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). METHOD: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. RESULTS: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomniapatients and GS (Cohen's κ = 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (κ=0.004). CONCLUSION: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
Authors: Christian S Guay; Alyssa K Labonte; Michael C Montana; Eric C Landsness; Brendan P Lucey; MohammadMehdi Kafashan; Simon Haroutounian; Michael S Avidan; Emery N Brown; Ben Julian A Palanca Journal: Nat Sci Sleep Date: 2021-03-04