Out of 60 million Americans suffering from sleep disorder, an estimated 18 million have sleep apnea. According to the U.S. Department of Health & Human Services, sleep apnea is a chronic condition that disrupts a patient’s sleep. While the annual cost of treating sleep apnea patients in the United States is approximately $3.18 billion (including screening costs) it is estimated that untreated sleep apnea may cause $3.4 billion in additional medical costs. A polysomnography (PSG) is an all-night sleep study which monitors various physical functions during sleep including electrical activity of the heart, brain wave patterns, eye movement, muscle tone, body movements, and breathing. It is currently, the most accurate and sophisticated test for the diagnosis of sleep-disordered breathing (SDB), but also, the most expensive. The cost of an overnight sleep study is estimated between $900 and $3,000. In addition, the PSG is not mobile and has to be administered outside a patient’s home. The Long QT Syndrome (LQTS) is a rhythm disorder that causes erratic (unpredictable) heartbeats. The LQTS has been linked to patients with the most severe form of sleep apnea. If LQTS is left untreated, sudden cardiac death may occur. Different methods for the classification and detection of sleep apnea have been proposed, we offer the Long QT (LQT) interval that is too often left undiagnosed and untreated as an symptom of sleep apnea. Additionally we are offering the LQT interval as apnea event(s) in the computation of an apnea-hypopnea index (AHI).
We proposed the use of a Body Area Network as a Pre-Screening Surrogate to the Polysomnography (PSG) consisting of, a heart and activity monitor and pulse oximeter. A system that is cost-effective, mobile, non-invasive, and flexible. Initial analysis and validation was performed on a dataset taken from the MIT-BIH arrhythmia database of (10 to 35 data subjects) a typical sleep apnea population for age, gender and heart rate. A clinical evaluation of the proposed scheme was performed alongside overnight sleep studies at the University of Miami (UM) Sleep Center over a period of elven nights. A patient dataset of elven patients that underwent a PSG was selected.
|School:||Florida Atlantic University|
|School Location:||United States -- Florida|
|Source:||DAI-B 76/08(E), Dissertation Abstracts International|
|Subjects:||Computer Engineering, Computer science|
|Keywords:||Body area networks, Polysomnography, Sleep apnea|
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