Accurate scoring of the apnea–hypopnea index using a simple non‐contact breathing sensor

Abstract

Summary Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient’s sleep during the overnight testing. We investigated the use of breathing signals derived from non-contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral-nasal thermistor, chest belt, and abdominal belt). The Apnea-Hypopnea Index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44 = 0.37, p = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra-class correlation coefficient of 0.97 for the Apnea-Hypopnea Index and an intra-class correlation coefficient of 0.85 for the Respiratory Disturbance Index when compared to scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the Apnea-Hypopnea Index.

A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection

“…force sensors) placed under the supports of a bed have been shown to have great utility for non-contact detection of various aspects of sleep while an individual lies on the bed. In our lab, we have used load cells to detect lying position [6], distinguish between sleep and wake [7], and have even utilized load cell data to detect sleep apnea[8, 9]. Load cells have also been shown to be able to detect breathing [10, 11].…”

Applications of evolving technologies in sleep medicine

“…BEATTIE et al [78] demonstrated the feasibility of using unobtrusive load cells installed under the bed to measure AHI. Calibrated cells can even be used to monitor a patient's weight as well as the lying position of an individual.…”

Robust Ambient Multisensor Signal Fusion Towards Clinical Data Analytics

“…Chen et al cited reducing computational complexity as a main driver towards a single signal [32]. Recently, multisensor systems have once again gained traction, generally with between 4 to 6, but sometimes up to 30 sensors [34][35][36][37][38][39][40][41][42]. Kortelainen et al originally embedded 160 sensors in a bed [40], but later reduced the number of sensors to just eight [43].…”

“…When the goal is to produce information from a group of signals, fusion can occur at a variety of points in the process: data-level, feature-level, and/or decision-level [113]. features using a Bayesian classifier [34,42]. Respiratory rates may also be extracted in the same manner.…”

“…Sleep disordered breathing has been identified through detecting obstructive apnea, hypopneas, and central apneas. [34,42,129,130]. Finally periodic leg movements have been detected for screening of restless leg syndrome [38,131,132].…”