Mandibular Movements are a Reliable Noninvasive Alternative to Esophageal Pressure for Measuring Respiratory Effort in Patients with Sleep Apnea Syndrome.

Purpose: 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.

Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea.

BACKGROUND: Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). METHODS: Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. RESULTS: A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. CONCLUSION: MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.

Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study.

PURPOSE: 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.

The key role of the mandible in modulating airflow amplitude during sleep.

RATIONALE: Mandibular position and motion during sleep rely on the balance between mandibular elevators and depressors. We hypothesized that vertical mandibular position (VMP) modulates airflow amplitude during sleep. METHODS: VMP, tidal nasal flow pressure (NFP) and concurrent surface electromyographic activity of the masseters (sEMG-m) were recorded and processed by a customized algorithm from 100 polysomnographic fragments including a micro-arousal (25 obstructive sleep apnea patients). The relationship between mandibular position and changes in airflow was analysed. RESULT: Concurrent VMP and sEMG-m activity changes routinely occurred before a new steady state of airflow documented by NFP. Vertical mandible depression was associated with a median (95% CI) reduction in NFP of 40.9% (14.6%-71.3%, p = 0.007) while vertical mandible elevation and mouth closure were associated with a median (95% CI) relative increase in NFP after arousal of 52.6% (17.9%-56.2%, p = 0.001). CONCLUSION: Elevation and lowering of the mandible were associated with changes in masseteric EMG activity modulating airflow amplitude during sleep.

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea.

Importance: Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches. Objective: To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis. Design, Setting, and Participants: Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018. Main Outcomes and Measures: Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h. Results: Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, -1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, -0.69; 95% CI, -3.77 to 2.38 events/h). An Sr-RDI underestimation of -11.74 (95% CI, -20.83 to -2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively. Conclusions and Relevance: Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.

Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep.

Context: Accurate discrimination between obstructive and central hypopneas requires quantitative assessments of respiratory effort by esophageal pressure (OeP) measurements, which preclude widespread implementation in sleep medicine practice. Mandibular Movement (MM) signals are closely associated with diaphragmatic effort during sleep. Objective: We aimed at reliably detecting obstructive off central hypopneas events using MM statistical characteristics. Methods: A bio-signal learning approach was implemented whereby raw MM fragments corresponding to normal breathing (NPB; n = 501), central (n = 263), and obstructive hypopneas (n = 1861) were collected from 28 consecutive patients (mean age = 54 years, mean AHI = 34.7 n/h) undergoing in-lab polysomnography (PSG) coupled with a MM magnetometer, and OeP recordings. Twenty three input features were extracted from raw data fragments to explore distinctive changes in MM signals. A Random Forest model was built upon those input features to classify the central and obstructive hypopnea events. External validation and interpretive analysis were performed to evaluate the model's performance and the contribution of each feature to the model's output. Results: Obstructive hypopneas were characterized by a longer duration (21.9 vs. 17.8 s, p < 10-6), more extreme low values (p < 10-6), a more negative trend reflecting mouth opening amplitude, wider variation, and the asymmetrical distribution of MM amplitude. External validation showed a reliable performance of the MM features-based classification rule (Kappa coefficient = 0.879 and a balanced accuracy of 0.872). The interpretive analysis revealed that event duration, lower percentiles, central tendency, and the trend of MM amplitude were the most important determinants of events. Conclusions: MM signals can be used as surrogate markers of OeP to differentiate obstructive from central hypopneas during sleep.

Mandibular Movement Analysis to Assess Efficacy of Oral Appliance Therapy in OSA.

RATIONALE: The respiratory effort index derived from vertical mandibular movements (MM-REI) is a potential marker of increased respiratory effort during sleep. We evaluated the effectiveness of mandibular advancement splint therapy using MM-REI, in comparison with the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). METHODS: Fifty-six subjects (median age, 47 years) with OSA treated with a custom mandibular advancement splint (Herbst appliance) were evaluated at the end of the titration procedure when snoring was reported absent by the sleep partner. We employed a magnetometer to capture mandibular movements (Brizzy; Nomics). Mandibular advancement splint efficacy was assessed as the percent change from baseline, using Bayesian multilevel models. RESULTS: At the end of titration, all indices of OSA severity decreased compared with baseline: AHI (-48.9% to -71.1%), ODI (-49.5% to -77.2%), with obstructive hypopnea index and MM-REI showing the largest responses (-70.6% to -88.5% and -69.5% to -96.3%, respectively). MM-REI normalization via reductions in both mandibular movement event rate and duration accurately reflected efficacy of the appliance. CONCLUSIONS: The reduction of vertical respiratory mandibular movements estimated by MM-REI and sleep respiratory effort duration accompanied the decrease in obstructive hypopneas, AHI, and ODI when snoring resolved in subjects with OSA treated with an optimally titrated mandibular advancement splint.

Mandibular Movements As Accurate Reporters of Respiratory Effort during Sleep: Validation against Diaphragmatic Electromyography.

CONTEXT: Mandibular movements (MM) are considered as reliable reporters of respiratory effort (RE) during sleep and sleep disordered breathing (SDB), but MM accuracy has never been validated against the gold standard diaphragmatic electromyography (EMG-d). OBJECTIVES: To assess the degree of agreement between MM and EMG-d signals during different sleep stages and abnormal respiratory events. METHODS: Twenty-five consecutive adult patients with SDB were studied by polysomnography (PSG) that also included multipair esophageal diaphragm electromyography and a magnetometer to record MM. EMG-d activity (microvolt) and MM (millimeter) amplitudes were extracted by envelope processing. Agreement between signals amplitudes was evaluated by mixed linear regression and cross-correlation function and in segments of PSG including event-free and SDB periods. RESULTS: The average total sleep time was 370 ± 18 min and the apnea hypopnea index was 24.8 ± 5.2 events/h. MM and EMG-d amplitudes were significantly cross-correlated: median r (95% CI): 0.67 (0.23-0.96). A mixed linear model showed that for each 10 µV of increase in EMG-d activity, MM amplitude increased by 0.28 mm. The variations in MM amplitudes (median range: 0.11-0.84 mm) between normal breathing, respiratory effort-related arousal, obstructive, mixed, and central apnea periods closely corresponded to those observed with EMG-d activity (median range: 2.11-8.23 µV). CONCLUSION: MM amplitudes change proportionally to diaphragmatic EMG activity and accurately identify variations of RE during normal sleep and SDB.

Monitoring mandibular movements to detect Cheyne-Stokes Breathing.

BACKGROUND: The patterns of mandibular movements (MM) during sleep can be used to identify increased respiratory effort periodic large-amplitude MM (LPM), and cortical arousals associated with "sharp" large-amplitude MM (SPM). We hypothesized that Cheyne Stokes breathing (CSB) may be identified by periodic abnormal MM patterns. The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard. The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard. METHODS: In 573 consecutive patients attending an in-laboratory PSG for suspected sleep disordered breathing (SDB), MM signals were acquired using magnetometry and scored manually while blinded from the PSG signal. Data analysis aimed to verify the concordance between the CSB identified by PSG and the presence of LPM or SPM. The data were randomly divided into training and validation sets (985 5-min segments/set) and concordance was evaluated using 2 classification models. RESULTS: In PSG, 22 patients (mean age ± SD: 65.9 ± 15.0 with a sex ratio M/F of 17/5) had CSB (mean central apnea hourly indice ± SD: 17.5 ± 6.2) from a total of 573 patients with suspected SDB. When tested on independent subset, the classification of CSB based on LPM and SPM is highly accurate (Balanced-accuracy = 0.922, sensitivity = 0.922, specificity = 0.921 and error-rate = 0.078). Logistic models based odds-ratios for CSB in presence of SPM or LPM were 172.43 (95% CI: 88.23-365.04; p < 0.001) and 186.79 (95% CI: 100.48-379.93; p < 0.001), respectively. CONCLUSION: CSB in patients with sleep disordered breathing could be accurately identified by a simple magnetometer device recording mandibular movements.

Mandibular position and movements: Suitability for diagnosis of sleep apnoea.

BACKGROUND AND OBJECTIVE: Mandibular movements (MMs) and position during sleep reflect respiratory efforts related to increases in upper airway resistance and micro-arousals. The study objective was to assess whether MM identifies sleep-disordered breathing (SDB) in patients with moderate to high pre-test probability. METHODS: This was a prospective study of 87 consecutive patients referred for an in-laboratory sleep test. Magnetometer-derived MM signals were incorporated into standard polysomnography (PSG). Respiratory events detected with MM analysis were compared with PSG for respiratory disturbance index (RDI) with a blinded scoring. All records were scored manually according to American Academy of Sleep Medicine rules. Primary outcome was to rule-in obstructive sleep apnoea syndrome (OSAS) defined as RDI cut-off value ≥5 or 15/h total sleep time (TST). RESULTS: High concordance emerged between MM and PSG-derived RDI with high temporal coincidence between events (R2 = 0.906; P < 0.001). The mean diagnostic accuracy of MM for OSAS using RDI MM cut-off values of 5.9 and 13.5 was 0.935 (0.86-0.97) and 0.913 (0.84-0.95), with a mean positive likelihood ratio (LLR+) of 3.73 (2.7-20.4) and 8.46 (2.3-31.5), respectively. Receiver operating characteristic (ROC) curves at PSG cut-off values of 5 and 15/h TST had areas under the curve (AUC) of 0.96 (95% CI: 0.89-0.99) and 0.97 (95% CI: 0.91-0.99) (P < 0.001), respectively. MM analysis accurately identified SDB at different levels of severity. CONCLUSION: RDI assessed by MM is highly concordant with PSG, suggesting a role of ambulatory MM recordings to screen for SDB in patients with moderate to high pre-test probability.

Mandibular movements identify respiratory effort in pediatric obstructive sleep apnea.

STUDY OBJECTIVES: Obstructive sleep apnea-hypopnea (OAH) diagnosis in children is based on the quantification of flow and respiratory effort (RE). Pulse transit time (PTT) is one validated tool to recognize RE. Pattern analysis of mandibular movements (MM) might be an alternative method to detect RE. We compared several patterns of MM to concomittant changes in PTT during OAH in children with adenotonsillar hypertrophy. PARTICIPANTS: 33 consecutive children with snoring and symptoms/signs of OAH. MEASUREMENTS: MMs were measured during polysomnography with a magnetometer device (Brizzy Nomics, Liege, Belgium) placed on the chin and forehead. Patterns of MM were evaluated representing peak to peak fluctuations > 0.3 mm in mandibular excursion (MML), mandibular opening (MMO), and sharp MM (MMS), which closed the mouth on cortical arousal (CAr). RESULTS: The median (95% CI) hourly rate of at least 1 MM (MML, or MMO, or MMS) was 18.1 (13.2-36.3) and strongly correlated with OAHI (p = 0.003) but not with central apnea-hypopnea index (CAHI; p = 0.292). The durations when the MM amplitude was > 0.4 mm and PTT > 15 ms were strongly correlated (p < 0.001). The mean (SD) of MM peak to peak amplitude was larger during OAH than CAH (0.9 ± 0.7 mm and 0.2 ± 0.3 mm; p < 0.001, respectively). MMS at the termination of OAH had larger amplitude compared to MMS with CAH (1.5 ± 0.9 mm and 0.5 ± 0.7 mm, respectively, p < 0.001). CONCLUSIONS: MM > 0.4 mm occurred frequently during periods of OAH and were frequently terminated by MMS corresponding to mouth closure on CAr. The MM findings strongly correlated with changes in PTT. MM analysis could be a simple and accurate promising tool for RE characterization and optimization of OAH diagnosis in children.