This clinical study aimed to identify stereotypical mandibular movements (MM) in patients with sleep bruxism (SBx) and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence based approach.
This was a prospective, observational study of 67 suspected OSA patients in whom in-lab PSG with masseter EMG was performed with simultaneous MM recordings. The study showed that MM activity can accurately detect RMMA episodes with a very good agreement when compared to the gold standard, i.e., in-lab PSG.
In conclusion, SBx can be reliably identified, quantified, and characterized with MM when subjected to automated analysis supported by artificial intelligence technology. Informative sleep MM provides a unique opportunity to easily detect RMMA and confirm a clinical diagnosis of SBx in conjunction with clinical history, and more importantly when OSA is a comorbid condition.