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
ContextMandibular 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).ObjectivesTo assess the degree of agreement between MM and EMG-d signals during different sleep stages and abnormal respiratory events.MethodsTwenty-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.ResultsThe 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).ConclusionMM amplitudes change proportionally to diaphragmatic EMG activity and accurately identify variations of RE during normal sleep and SDB.
Study Objectives: Obstructive sleep apnea-hypopnea (OAH) diagnosis in children is based on the quantifi cation of fl ow 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. Methods: 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 fl uctuations > 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 fi ndings 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.
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.
Bruxism is a heterogeneous condition related to various underlying mechanisms, including the presence of OSA. This case report illustrates that sleep mandibular movement monitoring and analysis could provide a useful opportunity for detection of both sleep bruxism and respiratory effort. The current case suggests that tracking of respiratory effort could enable evaluation of bruxism and its potential interactions. Successful treatment of sleep-related respiratory effort may lead to improved or resolution of bruxism in cases where such a causal relationship does exist. CHEST 2020; 157(3):e59-e62
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