Correlation between Apnea Severity and Sagittal Cephalometric Features in a Population of Patients with Polysomnographically Diagnosed Obstructive Sleep Apnea

Background and Objective: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder featuring a repeated closure of the upper airway during sleep. Craniofacial anatomy is a potential risk and worsening factor for OSA. This study aims to assess the relationship between cephalometric features of craniofacial morphology and OSA severity in a population of patients with OSA. Material and Methods: A sample of forty-two patients (n = 42, M = 76%, mean age = 57.8 ± 10.8) with a polysomnographically (PSG) confirmed diagnosis of OSA were recruited and underwent cephalometric evaluation of 16 cephalometric variables. In addition, the apnea–hypopnea index (AHI), oxygen desaturation (SatMin), Epworth sleepiness scale (ESS), and body mass index (BMI) were assessed. Then t-tests were performed to compare the values of all cephalometric variables between two AHI severity-based groups (mild-to-moderate = AHI ≤ 30; severe = AHI > 30). Single- and multiple-variable regression analyses were performed to assess the associations between AHI scores and cephalometric features. Results: Mean AHI, SatMin, and BMI were 31.4 ev/h, 78.7%, and 28.1, respectively. The cephalometric variables were not significantly different between the two OSA-severity groups (p > 0.05). Multiple-variable regression analyses showed that gonial angle and nasopharynx space were negatively associated with AHI, explaining 24.6% of the total variance. Conclusion: This investigation reported that severity of AHI scores in patients with OSA showed a negative correlation with gonial angle and nasopharynx space. As a general remark, although maxillofacial anatomy can be a predisposing factor for OSA, disease severity depends mainly upon other variables

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism: A scoping review

Background: Ambulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients. Objectives: (a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited-channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices. Method: A scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes. Results: Seventy-eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA). Conclusion: Adequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA