Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm “Nox Body Sleep™”

Abstract

Damien Leger,1,2 Maxime Elbaz1,2 1Université Paris Cité, (VIFASOM), ERC 7330 VIgilance FAtigue SOMmeil, Paris, France; 2Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, FranceCorrespondence: Damien Leger, VIFASOM Université Paris CitéCentre du Sommeil et de la Vigilance Hôtel-Dieu de Paris, 1 place du Parvis Notre Dame, Paris Cedex 04, Paris, 75181, France, Tel [phone], Email [email protected]: The COVID-19 pandemic has influenced clinical sleep protocols with stricter hospital disinfection requirements. Facing these new rules, we tested if a new artificial intelligence (AI) algorithm: The Nox BodySleep™ (NBS) developed without airflow signals for the analysis of sleep might assess pertinently sleep in patients with Obstructive Sleep Apnea (OSA) and chronic insomnia (CI) as a control group, compared to polysomnography (PSG) manual scoring.Patients-Methods: NBS is a recurrent neural network model that estimates Wake, NREM, and REM states, given features extracted from activity and respiratory inductance plethysmography (RIP) belt signals (Nox A1 PSG). Sleep states from 139 PSG studies (CI N = 72; OSA N = 67) were analyzed by NBS and compared to manually scored PSG using positive percentage agreement, negative percentage agreement, and overall agreement metrics. Similarly, we compared common sleep parameters and OSA severity using sleep states estimated by NBS for each recording and compared to manual scoring using Bland-Altman analysis and intra-class correlation coefficient.Results: For 127,170 sleep epochs, an overall agreement of 83% was reached for Wake, NREM and REM states (92% for REM states in CI patients) between NBS and manually scored PSG. Overall agreement for estimating OSA severity was 100% for moderate-severe OSA and 91% for minimal OSA. The absolute errors of the apnea–hypopnea index (AHI) and total sleep time (TST) were significantly lower for the NBS compared to no scoring of sleep. The intra-class correlation was higher for AHI and significantly higher for TST using the NBS compared to no scoring of sleep.Conclusion: NBS gives sleep states, parameters and AHI with a good positive and negative percentage agreement, compared with manually scored PSG.Keywords: PSG, chronic insomnia, OSA, machine learning, automatic sleep staging, artificial intelligence

Keywords

• psg-chronic insomnia-osa-machine learning-automatic sleep staging- artificial intelligence