This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The diagnosis of obstructive sleep apnea/hypopnea syndrome (OSAHS) is usually made from a full-night polysomnography performed in the sleep laboratory with complete monitoring of sleep, oxygen saturation and respiration. This process is both time consuming and expensive. In addition, increasing numbers of patients referred to sleep centers, particularly for symptoms of obstructive sleep apnea syndrome, produce delays of up to 1 year in some centers in the performance of this study. As a result, the development of a simplified accurate method of diagnosing OSAHS is highly desirable. The Apnea Risk Evaluation System (ARES) uses a multivariate approach that integrates a self-applied, single-site device to continuously record a) forehead pulse oximetry and pulse rate; b) nasal airflow and snoring; and c) head movements and position; combined with a validated questionnaire based on anthropomorphic and clinical information. The investigators and others have recently validated the diagnostic utility of the ARES unicorder by comparing laboratory polysomnography to unattended limited-channel in-home studies obtained using the ARES Unicorder. However, one of the arguments against the use of this limited monitoring posed by the sleep community is the absence of EEG scored sleep in the ARES unicorder. The goal of the proposed research is to obtain recordings of the ARES with an additional EEG channel, and to develop and test an algorithm to detect sleep using this signal. The ARES signals will be acquired concurrently with the standard signals acquired during laboratory polysomnography (PSG). The sleep-staging hypograms derived from the electrophysiological recordings collection during full polysomnography will be used to confirm the accuracy of the ARES automated algorithms for the staging of sleep. If a high degree of diagnostic accuracy can be demonstrated, the ARES system would provide an unattended, low-cost alternative diagnostic strategy for patients with OSAHS.
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