Background: The gold standard for diagnosing sleep bruxism (SB) and obstructive sleep apnea (OSA) is polysomnography (PSG). However, a final hypermotor muscle activity often occurs after apnea episodes, which can confuse the diagnosis of SB when using portable electromyography (EMG) devices. This study aimed to compare the number of SB episodes obtained from PSG with manual analysis by a sleep expert, and from a manual and automatic analysis of an EMG and electrocardiography (EKG) device, in a population with suspected OSA. Methods: Twenty-two subjects underwent a polysomnographic study with simultaneous recording with the EMG-EKG device. SB episodes and SB index measured with both tools and analyzed manually and automatically were compared. Masticatory muscle activity was scored according to published criteria. Patients were segmented by severity of OSA (mild, moderate, severe) following the American Academy of Sleep Medicine (AASM) criteria. ANOVA and the Bland–Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the intraclass correlation coefficient (ICC). Results: On average, the total events of SB per night in the PSG study were (8.41 ± 0.85), lower than the one obtained with EMG-EKG manual (14.64 ± 0.76) and automatic (22.68 ± 16.02) analysis. The mean number of SB episodes decreases from the non-OSA group to the OSA group with both PSG (5.93 ± 8.64) and EMG-EKG analyses (automatic = 22.47 ± 18.07, manual = 13.93 ± 11.08). However, this decrease was minor in proportion compared to the automatic EMG-EKG analysis mode (from 23.14 to 22.47). The ICC based on the number of SB episodes in the segmented sample by severity degree of OSA along the three tools shows a moderate correlation in the non-OSA (0.61) and mild OSA (0.53) groups. However, it is poorly correlated in the moderate (0.24) and severe (0.23) OSA groups: the EMG-EKG automatic analysis measures 14.27 units more than PSG. The results of the manual EMG-EKG analysis improved this correlation but are not good enough. Conclusions: The results obtained in the PSG manual analysis and those obtained by the EMG-EKG device with automatic and manual analysis for the diagnosis of SB are acceptable but only in patients without OSA or with mild OSA. In patients with moderate or severe OSA, SB diagnosis with portable electromyography devices can be confused due to apneas, and further study is needed to investigate this.

Background: The gold standard for the diagnosis of sleep bruxism (SB) and obstructive sleep apnea (OSA) is Polysmnography (PSG). At the end of the apnea episodes there is frequently a final hy-permotor muscle activity that could act as a confusion factor in the diagnosis of SB with the elec-tromyography portable devices. The aim of this study was to compare the concordance on the number of episodes of SB in a population with OSA, between the diagnosis obtained by PSG, an-alyzed manually by a neurophysiologist and that obtained manually and automatically by a portable electromyography (EMG) and electrocardiography (EKG) device. Methods: Twenty-three subjects underwent one night of polysomnographic study with simulta-neous recording with the EMG-EKG device. The variables referring to the number of episodes and the SB index measured with both tools and analyzed in the manual and automatic modes were compared. Masticatory muscle activity was scored according to published criteria. The sample was segmented by severity of OSA according to AASM criteria. ANOVA, correlations, and the Bland–Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the ICC. Results: The total events of SB per night in the PSG study were on average (8.17), lower than the one obtained with EMG-EKG manual analysis (14.13) and automatic (29.26). Both the SB PSG and Manual EMG-EKG episodes decrease from non-OSA (PSG = 16 ± 13,55, EMG-EKG = 16,83 ±11,58) to severe OSA (PSG = 3,14 ± 4,26, EMG-EKG = 9,86 ± 8,09). However, in the case of automatic EMG-EKG mode: the number of SB episodes in severe OSA doubled (41,23 ± 12,50) with respect to non OSA (24,50 ± 12,19). On average: the EMG-EKG device Automatic analysis measures 21.08 units more than PSG. The results with the manual EMG-EKG analysis improved. Conclusion: There is no concordance between the results obtained in the PSG neurophysiologic analysis and those obtained by means of the EMG-EKG device automatic and manual analysis for the diagnosis of SB in a population mostly with OSA. The OSA could act as a confusion factor in the diagnosis of SB with the electromyography portable devices, but further study is needed.