View clinical trials related to Sleep Disordered Breathing.
Filter by:This study is being undertaken to collect data from Respironics Inc's BiPAP Auto Servo Ventilation 3 (autoSV3) and compare with data from Respironics, Inc's BiPAP autoSV2, to confirm that the algorithms in the BiPAP autoSV3 device can safely and effectively treat participants experiencing Complex Sleep Apneas (Comp SAS) no worse than its predecessor, the BiPAP auto Servo ventilation 2 (autoSV2) device. This will be determined using a comparative, randomized design with the participants blinded to the therapy. Additionally, attempts will be made to blind the central scorer(s) with respect to which device is in use.
Patients with sleep disordered breathing (SDB), also called sleep apnea, experience nighttime disrupted sleep and, because they stop breathing for short periods during the night, do not get sufficient oxygen to their brains. This can frequently result in daytime impairments including difficulties with memory. The state-of-the-art treatment for SDB is Continuous Positive Airway Pressure (CPAP). Many non-demented SDB patients who are successfully treated with CPAP show improvement in memory and other cognitive functions. Data have shown that patients with Parkinson's disease have a high rate of SDB. Patients with Parkinson's disease also often have problems with memory. This study will test the effects of treating SDB among patients with Parkinson's disease and SDB. Specifically, the study will test the effect of CPAP treatment on SDB and sleep; the effect of CPAP treatment on daytime sleepiness, cognition, overall quality of life and mood; the effect of CPAP treatment on the frequency of symptoms of REM behavior disorder and restless legs syndrome; the effect of continued CPAP use (beyond the six weeks of the study) on SDB, sleep, cognition, mood and quality of life; whether the study-partner feels that CPAP improves the patient's sleep, mood and overall functioning; whether study-partners feel that their own sleep, mood and overall functioning improve as the patient's sleep improves both during the 6-week protocol and at follow-up for those opting to continue using CPAP.
This study will assess in a double blind placebo controlled fashion the effects of a 12 week course of oral montelukast/placebo on polysomnographic and radiological findings and will characterize the systemic (serum,urine) and local (upper airway collected biological samples) inflammatory response in children (2-10 years of age) with sleep disordered breathing, fittingthe inclusion and exclusion criteria.
The purpose of this research is to study and improve the methods used to detect childhood breathing problems during sleep that can affect daytime behavior at home and school. Early diagnosis of these sleep disorders may allow doctors to treat children at a time when the consequences can still be reversed.
Sleep-disordered breathing (SDB) in children may be responsible for disruptive daytime behaviors such as inattention and hyperactivity. Many children undergo tonsillectomy for SDB and disruptive daytime behaviors. However, the link between SDB and disruptive behavior is not clearly understood. This study will evaluate the relationship between SDB and disruptive behavior.
The diagnosis and treatment of sleep disordered breathing have come to the forefront of clinical medicine following recognition of the high prevalence and associated morbidity of sleep apnea. The effects on quality of life as well as societal costs have been well documented. The NYU Sleep Research Laboratory has spent the last several years working on the problem of improving the diagnosis of mild sleep disordered breathing which manifests as the upper airway resistance syndrome. Our approach has been to develop a non-invasive technique to detect increased upper airway resistance directly from analysis of the airflow signal. A characteristic intermittent change of the inspiratory flow contour, which is indicative of the occurrence of flow limitation, correlates well with increased airway resistance. Currently all respiratory events are identified manually and totaled. This is time consuming and subject to variability. The objective of the present project is to improve upon the manual approach by implementing an artificially intelligent system for the identification and quantification of sleep disordered breathing based solely on non-invasive cardiopulmonary signals collected during a routine sleep study. The utility of other reported indices of sleep disordered breathing obtained during a sleep study will be evaluated. Successful development of an automated system that can identify and classify upper airway resistance events will simplify, standardize and improve the diagnosis of sleep disordered breathing, and greatly facilitate research and clinical work in this area. Using a physiological based determination of disease should allow better assessment of treatment responses in mild disease.