The long-term goal of our project is to develop tools that can be used to diagnose and assess sleep disorders unobtrusively in a patient's home. Sleep disorders and sleep deprivation are significant public health problems. The U.S. Institute of Medicine estimates that 70 million Americans suffer from chronic, treatable sleep disorders. 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 disruption of sleep. The traditional gold standard for diagnosing and monitoring these disorders is overnight polysomnography (PSG). Unfortunately PSG is an expensive, obtrusive, and inconvenient test in which multiple sensors are attached to patients who are already struggling with sleep. A simpler tool used to screen for sleep disordered breathing (SDB) in a patient's home over multiple nights would help clinicians decide if PSG is indicated, providing a much needed and currently unavailable window into a patient's apnea status over multiple nights in their natural sleep environment. In this study, we will continue our efforts to understand how load cells could be used for this purpose. We have used load cells under the supports of the bed to quantify the frequency and severity of apneas and hypopneas. We now seek to understand how this system could be simplified and used effectively in a home setting.
Our Specific Aims are: (1) to develop improved algorithms to detect lying position on the bed during load cell collection~ (2) develop algorithms to separate the breathing and movement signals from two individuals sharing a bed~ and (3) to determine the most effective minimal configuration of load cells (number, location, and loading) that can be used to accurately measure the severity of sleep apnea.
Estimates of the prevalence of sleep disorders in the US range from 50 to 70 million people, and as many as 9% of middle-aged American men suffer from sleep disordered breathing. The direct cost of treating sleep disorders has been estimated in the range of $30-50 billion per year~ indirect costs including absenteeism from work and fatigue-related accidents are estimated to be $210 billion. The proposed study would create a tool allowing unobtrusive in-home assessment of sleep-disordered breathing, for screening patients and following treatment.
|Mosquera-Lopez, Clara; Leitschuh, Joseph; Condon, John et al. (2018) In-Home Sleep Apnea Severity Classification using Contact-free Load Cells and an AdaBoosted Decision Tree Algorithm. Conf Proc IEEE Eng Med Biol Soc 2018:6044-6047|
|Beattie, Zachary T; Jacobs, Peter G; Riley, Thomas C et al. (2015) A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection. Conf Proc IEEE Eng Med Biol Soc 2015:8111-4|
|Beattie, Zachary T; Hayes, Tamara L; Guilleminault, Christian et al. (2013) Accurate scoring of the apnea-hypopnea index using a simple non-contact breathing sensor. J Sleep Res 22:356-62|
|Adami, Adriana M; Adami, André G; Hayes, Tamara L et al. (2012) A Gaussian model for movement detection during sleep. Conf Proc IEEE Eng Med Biol Soc 2012:2263-6|
|Austin, Daniel; Beattie, Zachary T; Riley, Thomas et al. (2012) Unobtrusive classification of sleep and wakefulness using load cells under the bed. Conf Proc IEEE Eng Med Biol Soc 2012:5254-7|
|Beattie, Zachary T; Hagen, Chad C; Hayes, Tamara L (2011) Classification of lying position using load cells under the bed. Conf Proc IEEE Eng Med Biol Soc 2011:474-7|
|Adami, Adriana M; Adami, Andre G; Schwarz, Gilmar et al. (2010) A subject state detection approach to determine rest-activity patterns using load cells. Conf Proc IEEE Eng Med Biol Soc 2010:204-7|
|Beattie, Zachary T; Hagen, Chad C; Pavel, Misha et al. (2009) Classification of breathing events using load cells under the bed. Conf Proc IEEE Eng Med Biol Soc 2009:3921-4|