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 Tech of the Week
Each week we feature special technologies. Take a moment to view the other Tech of the Week articles, you may find one that meets a present need.
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 Tech of the Week

Predict Sleep Apnea without Overnight Study

Predictive Technologies, LLC.

apnea patterns

Obstructive Sleep Apnea (OSA) is a medical condition that causes the airway to collapse during sleep, severely restricting or completely blocking airflow as frequently as 5-300 times per hour. The impact of this condition is not only daytime sleepiness as a result of disturbed sleep, but can include hypertension, heart disease, depression and stroke. Motor vehicle and industrial accidents occur with significantly greater frequency and the inability to be vigilant impacts business and personal life immeasurably.

It is estimated that OSA contributes $16 Billion dollars in direct costs to society and indirect costs as high as $100 Billion. The prevalence of OSA is greater than diabetes or asthma, but because of inadequate tools for identifying the population at risk, ninety percent of sufferers remain undiagnosed and treated.

Predictive Technologies, LLC. (PTL) has developed the ASAP Advanced Sleep Apnea PredictorTM, a tool that can rapidly, accurately and non-invasively screen for the presence or absence of Obstructive Sleep Apnea. In addition, it provides a benchmark for effective disease management. ASAP requires only that an individual breathe through a standard oxygen nasal cannula for approximately 5 minutes while awake and seated. The breathing pattern is captured by a transducer and sent to a processor, which analyzes the pattern using sophisticated non-linear algorithms. Within several minutes, the ASAP identifies patients who are likely to have OSA.

PTLĀ“s technology is based on technology developed by the Naval Undersea Warfare Center (US) to detect foreign objects in the open ocean. Highly sophisticated non-linear mathematical analysis picks out minute aberrations from the more regular patterns associated with an environment. When these algorithms are applied to breathing patterns, subjects with OSA are readily distinguished from non-sufferers. Initial studies demonstrate a 90% accuracy rate for identifying with patients OSA.

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