Rationale: Coronavirus disease (COVID-19) is an ongoing pandemic, in which obesity, hypertension, and diabetes have been linked to poor outcomes. Obstructive sleep apnea (OSA) is associated with these conditions and may influence the prognosis of adults with COVID-19. Objectives: To determine the effect of OSA on clinical outcomes in patients with COVID-19. Methods: The current prospective observational study was conducted in three hospitals in Istanbul, Turkey from March 10 to June 22, 2020. The participants were categorized as high-risk or low-risk OSA according to the Berlin questionnaire that was administered in the out-patient clinic, in hospital, or shortly after discharge from hospital blinded to the clinical outcomes. A modified high-risk (mHR)–OSA score based on the snoring patterns (intensity and/or frequency), breathing pauses, and morning/daytime sleepiness, without taking obesity and hypertension into account, were used in the regression models. Results: The primary outcome was the clinical improvement defined as a decline of two categories from admission on a 7-category ordinal scale that ranges from 1 (discharged with normal activity) to 7 (death) on Days 7, 14, 21, and 28, respectively. Secondary outcomes included clinical worsening (an increase of 1 category), need for hospitalization, supplemental oxygen, and intensive care. In total, 320 eligible patients (median [interquartile range] age, 53.2 [41.3–63.0] yr; 45.9% female) were enrolled. In all, 121 (37.8%) were categorized as known (n = 3) or high-risk OSA (n = 118). According to the modified scoring, 70 (21.9%) had mHR-OSA. Among 242 patients requiring hospitalization, clinical improvement within 2 weeks occurred in 75.4% of the mHR-OSA group compared with 88.4% of the modified low-risk–OSA group (P = 0.014). In multivariate regression analyses, mHR-OSA (adjusted odds ratio [OR], 0.42; 95% confidence interval [CI], 0.19–0.92) and male sex (OR, 0.39; 95% CI, 0.17–0.86) predicted the delayed clinical improvement. In the entire study population (n = 320), including the nonhospitalized patients, mHR-OSA was associated with clinical worsening (adjusted hazard ratio, 1.55; 95% CI, 1.00–2.39) and with the need for supplemental oxygen (OR, 1.95; 95% CI, 1.06–3.59). Snoring patterns, especially louder snoring, significantly predicted delayed clinical improvement, worsening, need for hospitalization, supplemental oxygen, and intensive care. Conclusions: Adults with mHR-OSA in our COVID-19 cohort had poorer clinical outcomes than those with modified low-risk OSA independent of age, sex, and comorbidities. Clinical trial registered with www.clinicaltrials.gov (NCT04363333).

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

Accurate quantification of microsleep (MS) in drivers is crucial for preventing real-time accidents. We propose one-to-one correlation between events of high-fidelity driving simulator (DS) and corresponding brain patterns, unlike previous studies focusing general impact of MS on driving performance. Fifty professional drivers with obstructive sleep apnea (OSA) participated in a 50-minute driving simulation, wearing six-channel Electroencephalography (EEG) electrodes. 970 out-of-road OOR (microsleep) events (wheel and boundary contact ≥ 1s), and 1020 on-road OR (wakefulness) events (wheel and boundary disconnection ≥ 1s), were recorded. Power spectrum density, computed using discrete wavelet transform, analyzed power in different frequency bands and theta/alpha ratios were calculated for each event. We classified OOR (microsleep) events with higher theta/alpha ratio compared to neighboring OR (wakefulness) episodes as true MS and those with lower ratio as false MS. Comparative analysis, focusing on frontal brain, matched 791 of 970 OOR (microsleep) events with true MS episodes, outperforming other brain regions, and suggested that some unmatched instances were due to driving performance, not sleepiness. Combining frontal channels F3 and F4 yielded increased sensitivity in detecting MS, achieving 83.7% combined mean identification rate (CMIR), surpassing individual channel's MIR, highlighting potential for further improvement with additional frontal channels. We quantified MS duration, with 95% of total episodes lasting between 1 to 15 seconds, and pioneered a robust correlation (r = 0.8913, p<0.001) between maximum drowsiness level and MS density. Validating simulator's signals with EEG patterns by establishing a direct correlation improves reliability of MS identification for assessing fitness-to-drive of OSA-afflicted adults.

Previous reports from relatively small clinical cohorts have suggested that the clinical presentation of obstructive sleep apnea (OSA) differs between men and women. We aimed to explore sex differences in clinical and polysomnographic features of OSA in a large nationwide registry. Participants from the ongoing Turkish Sleep Apnea Database (TURKAPNE) Study from 34 centers were included in the current analysis. OSA was defined as an apnea-hypopnea index (AHI) ≥5 events/hour and was classified as mild, moderate, and severe according to AHI cut-offs 5, 15, and 30 events/hour, respectively. In all, 7130 patients (2259 women) were included. OSA was observed in 6323 (88.7 %), of whom 70.2 % were male and 29.8 % were female. In the OSA group, women were older (56.7 ± 11.9 vs. 49.5 ± 11.3 years; p < 0.001) and more obese (body mass index 34.3 ± 7.2 vs. 31.4 ± 5.6 kg/m²; p < 0.001) and had lower AHI (29.8 ± 24.1 vs. 36.8 ± 26.2 events/h; p < 0.001) than men. Loud snoring and witnessed apnea were more common in men than in women whereas women were more frequently presented with insomnia, headache, and mood changes. Women had significantly less total sleep time, less sleep efficiency, and longer sleep latency compared with men (p < 0.001 for each). Additionally, comorbid diseases such as diabetes mellitus, hypertension, asthma, psychiatric disorders, hypothyroidism as well as drug use were more common in women than in men independent of age and obesity (p < 0.05 for each). Our results suggest significant sex differences in clinical and polysomnographic features in this nationwide Turkish adult population. Women with OSA have more symptom burden and comorbidities despite having a less severe AHI.

Multichannel electroencephalography (EEG)-based drowsiness detection (DD) offers higher coverage but comes with increased computational demands, hardware requirements, and user discomfort, whereas single-channel devices are cost-effective and user-friendly but provide lower coverage. We hypothesized that an optimal channel with optimum paired EEG features could achieve coverage comparable to a multichannel system. Subject-specific, EEG-feature-specific thresholding techniques were introduced to classify 927 EEG epochs, derived from visual-based scoring through image processing of fifty drivers’ facial expressions during a 50-min driving simulation, using six individual EEG channels with paired features. Ten normalized EEG features were extracted per epoch using discrete wavelet transform (DWT), and seven thresholding techniques were applied to identify the most consistent method across subjects. Epochs were classified as drowsy or wakeful based on whether their normalized values exceeded or fell below a specific threshold. We then assessed the coverage of each channel by comparing EEG patterns with visual-based scoring. To determine the optimal feature pair for classifying each epoch in alignment with visual-based scoring, 45 feature combinations were evaluated. The pairing of power spectral density (PSD) alpha and PSD theta in channels Frontal4 (F4) and Occipital2 (O2) yielded the highest coverage, achieving 96.1% and 95% with corresponding accuracies of 95.4% and 94.7%, respectively. These results slightly surpassed the coverage achieved using six channels with a single feature, with increases of 1.47% for F4 and 0.32% for O2. Our study demonstrates that an optimal EEG channel with optimum paired EEG features can reduce channels from six to one, lowering computational demands for wearable DD devices. Graphical abstract

Background: Comorbid insomnia and obstructive sleep apnea (COMISA) is increasingly recognized as a clinically important sleep disorder phenotype with potential cardiometabolic consequences. However, its association with diabetes mellitus (DM) remains insufficiently characterized. Methods: This cross-sectional analysis included 12,715 adults with suspected obstructive sleep apnea (OSA) from the nationwide multicenter TURKAPNE cohort who underwent full-night polysomnography. Insomnia symptoms were assessed using standardized questionnaire items, and OSA was defined as apnea–hypopnea index (AHI) ≥ 5 events/hour. COMISA was defined as the coexistence of insomnia symptoms and OSA. DM was identified based on self-reported physician diagnosis or use of antidiabetic medications. Multivariable logistic regression was performed to evaluate independent variables associated with COMISA. Results: COMISA was present in 3,275 patients (25.7%). DM was significantly more prevalent among patients with COMISA compared with those without COMISA (22.8% vs. 15.4%, p < 0.001). Overall, COMISA was observed in 33.9% of diabetic individuals and 24.1% of non-diabetic individuals. In multivariable analysis adjusted for age, sex, body-mass-index, smoking status, education level, and comorbidities, DM remained independently associated with COMISA (adjusted OR 1.17; 95% CI 1.05–1.31; p = 0.006). Among COMISA patients, those with DM exhibited greater sleep fragmentation and more severe sleep-disordered breathing, including higher AHI (30.0 vs. 24.0 events/h) and oxygen desaturation index (26.0 vs. 20.0 events/h) ( p < 0.001). Conclusions: DM is independently associated with COMISA and with more severe sleep and respiratory disturbances within this phenotype. These findings highlight the need for integrated screening of sleep disorders in individuals with DM. Trial registration: ClinicalTrials gov (NCT02784977).