Quantitative analyses of jaw-opening muscle activity during the active phase of jaw-closing muscles in sleep bruxism

Understanding the pathophysiology of sleep bruxism based on human and animal studies: A narrative review

BACKGROUND

Sleep bruxism (SB) is a common sleep disorder that affects approximately 20% of children and 10% of adults. It may cause orodental problems, such as tooth wear, jaw pain, and temporal headaches. However, the pathophysiological mechanisms underlying SB remain largely unknown, and a definitive treatment has not yet been established.

HIGHLIGHT

Human studies involving polysomnography have shown that rhythmic masticatory muscle activity (RMMA) is more frequent in otherwise healthy individuals with SB than in normal individuals. RMMA occurs during light non-rapid eye movement (non-REM) sleep in association with transient arousals and cyclic sleep processes. To further elucidate the neurophysiological mechanisms of SB, jaw motor activities have been investigated in naturally sleeping animals. These animals exhibit various contractions of masticatory muscles, including episodes of rhythmic and repetitive masticatory muscle bursts that occurred during non-REM sleep in association with cortical and cardiac activation, similar to those found in humans. Electrical microstimulation of corticobulbar tracts may also induce rhythmic masticatory muscle contractions during non-REM sleep, suggesting that the masticatory motor system is activated during non-REM sleep via excitatory inputs to the masticatory central pattern generator.

CONCLUSION

This review article summarizes the pathophysiology of SB and putative origin of RMMA in both human and animal studies. Physiological factors contributing to RMMA in SB have been identified in human studies and may also be present in animal models. Further research is required to integrate the findings between human and animal studies to better understand the mechanisms underlying SB.

Multimodal transcutaneous auricular vagus nerve stimulation: An option in the treatment of sleep bruxism in a "polyvagal" context

OBJECTIVE

To consider the possible role of the vagus nerve (VN) in the pathophysiology of sleep bruxism (SB) and introduce a multimodal protocol of transcutaneous auricular stimulation of the VN in the treatment of SB patients.

METHODS

Ten patients with SB underwent four sessions of electric transcutaneous auricular vagus nerve stimulation (ta-VNS) in specific auricular areas. The patients were advised to manually stimulate the same areas between sessions. Masticatory muscle activity and sleep parameters were measured by a polysomnography (PSG) before and after the treatment. Heart rate variability (HRV) parameters were measured during each stimulation.

RESULTS

PSG analysis revealed a statistically significant reduction in tonic SB index and tonic contraction time. HRV parameters showed a statistically significant increase in mean values of the vagal tone after each session of stimulation. No side effect was reported.

CONCLUSION

The stimulation of the VN might have a role in the treatment of SB.

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.