Affiliation: OHSU Oregon Center for Aging and Technolgoy
Funding Period: 2010 - 2015
Funding Source: NHLBI (NIH)
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The objective of this proposal is to develop a methodology for unobtrusively measuring sleep disordered breathing (SDB) in a patient’s home. Specifically, we will develop algorithms for detecting SDB from data collected using load cells placed under the supports of the bed. Small movements in the body’s center of mass allow heart rate, breathing, and movements to be detected as changes in the relative load at each corner of the bed. We hypothesize that these changes in load can be used to quantify the frequency and severity of apneas and hypopneas.
Sleep disorders and sleep deprivation are significant public health problems. The U.S. Institute of Health estimates that 70 million Americans suffer from chronic, treatable sleep disorder. One of the most common and problematic sleep disorders is obstructive sleep apnea (OSA), where a partial collapse or obstruction of the pharyngeal airway results in intermittent reduction in blood oxygen saturation and sleep arousal. The traditional gold standard for diagnosing and monitoring these disorders is overnight polysomnography (PSG). Unfortunately PSG is expensive, obtrusive, and inconvenient. Patients who are already struggling with sleep are physically wired to several sensors and asked to sleep normally in a sleep lab. Also, these tests are not usually performed frequently enough to detect the night-to-night variance that many sleep disorders exhibit. A less expensive tool that can be used to screen for SDB in a patient’s home over multiple nights would help clinicians decide if polysomnography is indicated, and may provide important additional data about the nightly variance of the patient’s sleep problems.
Currently, mobile polysomnography can be done in the home, but it requires a technician to set it up and it continues to be an invasive procedure that disrupts normal sleep patterns. Although a number of researchers have suggested methods of assessing sleep unobtrusively, to date the only other technique that has been used to detect disrupted sleep clinically is body-worn accelerometry. This technology still requires correct use and compliance by the patient, and also cannot be used to measure respiration, which is an important parameter for assessing sleep apneas.
The long-term goal of our project is to develop a tool that can be used to diagnose and assess sleep disorders unobtrusively in a patient’s home. In the proposed study, we will develop algorithms for automatically identifying and quantifying central and obstructive apneas and hypopneas from bed sensor data. We will also determine if these sensors can be used to predict the severity of sleep disordered breathing and sleep hygiene in patients who are subsequently evaluated in a sleep clinic.