Cardiovascular and respiratory dynamics in patients with sleep apnea

Periodic limb movements during sleep in stroke/TIA: Prevalence, course, and cardiovascular burden

OBJECTIVE

To define the prevalence, time course, and associated factors of periodic limb movements during sleep (PLMS) in patients with ischemic stroke or TIA.

METHODS

Patients enrolled in the prospective Sleep-Disordered Breathing in Transient Ischemia Attack (TIA)/Ischemic Stroke and Continuous Positive Airway Pressure (CPAP) Treatment Efficacy (SAS-CARE) study underwent a double polysomnographic investigation in the acute and chronic phases after stroke/TIA, together with a MRI brain scan and a 24-hour blood pressure evaluation. The prevalence of PLMS in patients was compared with that in a matched sample of randomly selected healthy controls from the HypnoLaus cohort. One hundred sixty-nine recordings were performed in the acute phase and 191 after 3 months (210 recordings were obtained from the same 105 patients in both phases) and were compared to those of 162 controls.

RESULTS

The mean number of PLMS per hour and the percentage of participants with a PLMS index >10 and >15 per hour were similar between patients and controls. PLMS remained stable from the acute to the chronic phase after stroke. Factors positively associated with PLMS were age, body mass index, and history of hypertension. Blood pressure over 24 hours and the burden of cerebrovascular damage were similar between the groups with PLMS and without PLMS.

CONCLUSIONS

PLMS are equally frequent in patients with stroke/TIA and the general population. The absence of higher blood pressure values and of a greater vascular brain damage found in patients with PLMS compared to those without PLMS might be due to a greater use of antihypertensive medication among patients with PLMS, which corresponds to a higher prevalence of previous diagnosis of hypertension in these patients.

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.

Consistency of sleep restoration gain (SRG) as a measure for assessing sleep quality

We propose a new sleep quality measure that assesses the sleep restorative gain of a polysomnography sleep record. In this preliminary investigation, we derive this new measure from manually scored sleep hypnograms. We compare the proposed measure to classical sleep indices such as TST, SE, and ArI, and demonstrate its self-consistency and degree of correlation with these measures. Using 47 sleep records from publicly available sleep databases, we graphically and quantitatively demonstrate the effectiveness of the proposed measure in summarizing the hypnogram of a sleep record.