BACKGROUND
Sleep health is an incredibly important area of healthcare with the obstructive sleep apnea (OSA) market being estimated at almost 100 million dollars in 2017. Polysomnography (sleep study) is employed to diagnose OSA and other sleep-related disorders, but the data it collects is subject to noise. Improvement on analysis of automated data collection could be useful for enhance detection of sleep disorders as well as other health problems.
SUMMARY OF TECHNOLOGY
Researchers at OSU and Northern Kentucky University have developed a novel method of automatically detecting abnormalities from patient polysomnography data using control charts. Control charts can detect and monitor variability with each sleep stage transition. Physiological systems are in higher control in sleep-stage than wake stage, making polysomnography data from sleeping patients a prime candidate for automated diagnostics which can be pulled in real-time during an at-home study.
POTENTIAL AREAS OF APPLICATION
MAIN ADVANTAGES
STAGE OF DEVELOPMENT
This technology is currently available as a concept.
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