Abstract
We conducted a pilot study to evaluate the accuracy of a custom built non-contact pressure-sensitive device in diagnosing obstructive sleep apnea (OSA) severity as an alternative to in-laboratory polysomnography (PSG) and a Type 3 in-home sleep apnea test (HSAT). Fourteen patients completed PSG sleep studies for one night with simultaneous recording from our load-cell-based sensing device in the bed. Subjects subsequently installed pressure sensors in their bed at home and recorded signals for up to four nights. Machine learning models were optimized to classify sleep apnea severity using a standardized American Academy of Sleep Medicine (AASM) scoring of the gold standard studies as reference. On a per-night basis, our model reached a correct OSA detection rate of 82.9% (sensitivity = 88.9%, specificity = 76.5%), and OSA severity classification accuracy of 74.3% (61.5% and 81.8% correctly classified in-clinic and in-home tests, respectively). There was no difference in Apnea Hypopnea Index (AHI) estimation when subjects wore HSAT sensors versus load cells (LCs) only (p-value = 0.62). Our in-home diagnostic system provides an unobtrusive method for detecting OSA with high sensitivity and may potentially be used for long-term monitoring of breathing during sleep. Further research is needed to address the lower specificity resulting from using the highest AHI from repeated samples.
Highlights
Obstructive Sleep Apnea (OSA) is a prevalent medical condition that occurs when the upper airway becomes blocked repeatedly during sleep causing breathing to slow or completely stop
We show the performance of our sensing device across data collected in the sleep lab when patients wore PSG sensors, in-home data when patients wore home sleep apnea test (HSAT) sensors, and in-home data when patients did not wear HSAT sensors when presumably we would capture more of a typical night of sleep
We evaluated our prediction models on 13 PSG recordings corresponding to a single in-clinic sleep test and 22 HSAT recordings corresponding to one or two overnight sleep studies per subject
Summary
Obstructive Sleep Apnea (OSA) is a prevalent medical condition that occurs when the upper airway becomes blocked repeatedly during sleep causing breathing to slow or completely stop. It is estimated that approximately 15% of the population suffers from OSA [1,2], but despite its prevalence many clinically significant cases remain undiagnosed [3,4]. PSG is carried out at a sleep clinic where patients spend the night with multiple sensors attached to their body to measure physiologic metrics, including airflow, respiratory effort, blood oxygen saturation levels, electrical brain activity, eye movement, muscle activity, electrical heart activity, and snoring. These signals are analyzed by a trained clinician in order to detect the presence and duration of apnea and hypopnea events. There is a scarcity of specialized clinics and trained professionals that are available to diagnose OSA, which
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