Background: Sleep-disordered breathing (SDB) is highly prevalent after stroke and is considered to be a risk factor for poor post-stroke outcomes. The aim of this observational study was to evaluate the effect of nocturnal respiratory-related indices based on nocturnal respiratory polygraphy on clinical outcomes (including mortality and non-fatal events) in patients with ischemic stroke. Methods: A total of 328 consecutive patients (181 (55%) males, mean age 65.8 ± 11.2 years old) with confirmed ischemic stroke admitted to a stroke unit within 24 h after stroke onset were included in the analysis. All patients underwent standard diagnostic and treatment procedures, and sleep polygraphy was performed within the clinical routine in the first 72 h after admission. The long-term outcomes were assessed by cumulative endpoint (death of any cause, new non-fatal myocardial infarction, new non-fatal stroke/transient ischemic attack, emergency revascularization, emergency hospitalization due to the worsening of cardiovascular disease). A Cox-regression analysis was applied to evaluate the effects of nocturnal respiratory indices on survival. Results: The mean follow-up period comprised 12 months (maximal—48 months). Patients with unfavourable outcomes demonstrated a higher obstructive apnea-hypopnea index, a higher hypoxemia burden assessed as a percent of the time with SpO₂ < 90%, a higher average desaturation drop, and a higher respiratory rate at night. Survival time was significantly lower (30.6 (26.5; 34.7) versus 37.9 (34.2; 41.6) months (Log Rank 6.857, p = 0.009)) in patients with higher hypoxemia burden (SpO₂ < 90% during ≥2.1% versus <2.1% of total analyzed time). However, survival time did not differ depending on the SDB presence assessed by AHI thresholds (either ≥5 or ≥15/h). The multivariable Cox proportional hazards regression (backward stepwise analysis) model demonstrated that the parameters of hypoxemia burden were significantly associated with survival time, independent of age, stroke severity, stroke-related medical interventions, comorbidities, and laboratory tests. Conclusion: Our study demonstrates that the indices of hypoxemia burden have additional independent predictive value for long-term outcomes (mortality and non-fatal cardiovascular events) after ischemic stroke.

Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is growing interest in advanced EEG analysis that requires extensive preprocessing to improve the signal-to-noise ratio and specialized analysis algorithms. While many EEG software packages exist, sleep research has unique needs (e.g., specific artifacts, event detection). Currently, sleep investigators use different libraries for specific tasks in a 'fragmented' configuration that is inefficient, prone to errors, and requires the learning of multiple software environments. This complexity creates a barrier for beginners. Here, we present SleepEEGpy, an open-source Python package that simplifies sleep EEG preprocessing and analysis. SleepEEGpy builds on MNE-Python, PyPREP, YASA, and SpecParam to offer an all-in-one, beginner-friendly package for comprehensive sleep EEG research, including (i) cleaning, (ii) independent component analysis, (iii) sleep event detection, (iv) spectral feature analysis, and visualization tools. A dedicated dashboard provides an overview to evaluate data and preprocessing, serving as an initial step prior to detailed analysis. We demonstrate SleepEEGpy's functionalities using overnight high-density EEG data from healthy participants, revealing characteristic activity signatures typical of each vigilance state: alpha oscillations in wakefulness, spindles and slow waves in NREM sleep, and theta activity in REM sleep. We hope that this software will be adopted and further developed by the sleep research community, and constitute a useful entry point tool for beginners in sleep EEG research.

Objective. Sleep-disordered breathing (SDB) is a common cardiovascular risk factor. The aim of our study was to assess the occurrence and features of SDB in patients with acute supratentorial ischemic stroke. Design and methods. Patients 18-89 years of age with acute ischemic stroke admitted to the stroke intensive care unit within 24 hours after the symptom onset underwent respiratory monitoring during the first day of hospitalization. 1616 patients were screened between 2018 and 2021 years, respiratory monitoring was performed in 583 patients, and data from 281 patients [mean age 67 (30; 89) years, 146 males (52 %)] were included in the final analysis. Results. The mean respiratory disturbance index was 11,8 (0; 88)/h. SDB was detected in 182 patients (69,2 %), with mild severity in 28,6 %, moderate in 24,2 %, and severe in 47,2 %. Prevalent obstructive apnea type was observed in 71,1 %, central type in 14,2 %, and mixed type in 14,7 %. The TOAST stroke subtype distribution was the following: unspecified etiology was diagnosed in 52 %, cardioembolic in 26 %, atherothrombotic in 11 %, lacunar in 9 %, and other established etiology in 2 %. The majority of patients had mild stroke severity (89,4 %), moderate and severe stroke was diagnosed in 10,5 %. No significant differences in the main indices of the type and severity of sleep apnea were found in the groups based on severity and pathogenetic type of stroke. Conclusions. Our results correlate with the worldwide prevalence of SDB in patients with ischemic stroke (69,2 % and 71,1 %). Further analysis with inclusion of more patients with moderate and severe stroke is required, as well as a prospective study to assess the prognostic impact of SDB.