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Jean-Louis Pepin,1,* Nhat-Nam Le-Dong,2,* Valérie Cuthbert,3 Nathalie Coumans,3 Renaud Tamisier,1 Atul Malhotra,4 Jean-Benoit Martinot3,5 1HP2 Laboratory, Inserm U1300, University Grenoble Alpes, Grenoble, France; 2Sunrise, Namur, Belgium; 3Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, Belgium; 4University of California San Diego, La Jolla, CA, USA; 5Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium*These authors contributed equally to this workCorrespondence: Jean-Benoit Martinot, Centre du Sommeil et de la Vigilance, CHU-UCL Namur Site Ste Elisabeth, 15 Place Louise Godin, Namur, 5000, Belgium, Tel +32 495502608, Fax +32 81570754, Email martinot.j@respisom.bePurpose: Differentiation between obstructive and central apneas and hypopneas requires quantitative measurement of respiratory effort (RE) using esophageal pressure (PES), which is rarely implemented. This study investigated whether the sleep mandibular movements (MM) signal recorded with a tri-axial gyroscopic chin sensor (Sunrise, Namur, Belgium) is a reliable surrogate of PES in patients with suspected obstructive sleep apnea (OSA).Patients and Methods: In-laboratory polysomnography (PSG) with PES and concurrent MM monitoring was performed. PSGs were scored manually using AASM 2012 rules. Data blocks (n=8042) were randomly sampled during normal breathing (NB), obstructive or central apnea/hypopnea (OA/OH/CA/CH), respiratory effort-related arousal (RERA), and mixed apnea (MxA). Analyses were evaluation of the similarity and linear correlation between PES and MM using the longest common subsequence (LCSS) algorithm and Pearson’s coefficient; description of signal amplitudes; estimation of the marginal effect for crossing from NB to a respiratory disturbance for a given change in MM signal using a mixed linear-regression.Results: Participants (n=38) had mild to severe OSA (median AH index 28.9/h; median arousal index 23.2/h). MM showed a high level of synchronization with concurrent PES signals. Distribution of MM amplitude differed significantly between event types: median (95% confidence interval) values of 0.60 (0.16– 2.43) for CA, 0.83 (0.23– 4.71) for CH, 1.93 (0.46– 12.43) for MxA, 3.23 (0.72– 18.09) for OH, and 6.42 (0.88– 26.81) for OA. Mixed regression indicated that crossing from NB to central events would decrease MM signal amplitude by – 1.23 (CH) and – 2.04 (CA) units, while obstructive events would increase MM amplitude by +3.27 (OH) and +6.79 (OA) units (all p< 10− 6).Conclusion: In OSA patients, MM signals facilitated the measurement of specific levels of RE associated with obstructive, central or mixed apneas and/or hypopneas. A high degree of similarity was observed with the PES gold-standard signal.Keywords: obstructive sleep apnea, respiratory effort, mandibular movements, esophageal pressure

Jean-Benoit Martinot,1,2 Nhat-Nam Le-Dong,3 Valérie Cuthbert,1 Stéphane Denison,3 David Gozal,4 Gilles Lavigne,5 Jean-Louis Pépin6 1Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, 5000, Belgium; 2Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, 1200, Belgium; 3Sunrise, Namur, 5101, Belgium; 4Department of Child Health and Child Health Research Institute, University of Missouri, Columbia, MO, 65201, USA; 5Faculté de médecine dentaire, Université de Montréal, Montréal, Québec, H3C 3J7, Canada; 6HP2 Laboratory, Inserm U1042, University Grenoble Alpes, Grenoble, 38000, FranceCorrespondence: Jean-Benoit MartinotCentre du Sommeil et de la Vigilance, CHU UCL Namur Site Ste Elisabeth, 15, Place Louise Godin, Namur, 5000, BelgiumTel +32 81 720 411Fax +32 81 570 754Email martinot.j@respisom.bePurpose: Sleep bruxism (SBx) activity is classically identified by capturing masseter and/or temporalis masticatory muscles electromyographic activity (EMG-MMA) during in-laboratory polysomnography (PSG). We aimed to identify stereotypical mandibular jaw movements (MJM) in patients with SBx and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence (AI) based approach.Patients and Methods: This was a prospective, observational study of 67 suspected obstructive sleep apnea (OSA) patients in whom PSG with masseter EMG was performed with simultaneous MJM recordings. The system used to collect MJM consisted of a small hardware device attached on the chin that communicates to a cloud-based infrastructure. An extreme gradient boosting (XGB) multiclass classifier was trained on 79,650 10-second epochs of MJM data from the 39 subjects with a history of SBx targeting 3 labels: RMMA episodes (n=1072), micro-arousals (n=1311), and MJM occurring at the breathing frequency (n=77,267).Results: Validated on unseen data from 28 patients, the model showed a very good epoch-by-epoch agreement (Kappa = 0.799) and balanced accuracy of 86.6% was found for the MJM events when using RMMA standards. The RMMA episodes were detected with a sensitivity of 84.3%. Class-wise receiver operating characteristic (ROC) curve analysis confirmed the well-balanced performance of the classifier for RMMA (ROC area under the curve: 0.98, 95% confidence interval [CI] 0.97– 0.99). There was good agreement between the MJM analytic model and manual EMG signal scoring of RMMA (median bias − 0.80 events/h, 95% CI − 9.77 to 2.85).Conclusion: SBx can be reliably identified, quantified, and characterized with MJM when subjected to automated analysis supported by AI technology.Keywords: masticatory muscular activities, machine learning, jaw movement