https://orcid.org/0000-0003-0960-9632
![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
Abstract
Purpose
Due to lack of access and high cost of polysomnography, portable sleep apnea testing has been developed to diagnose sleep apnea. Despite being less expensive, and having fewer sensors and reasonable accuracy in identifying sleep apnea, such devices can be less accurate than polysomnography in detecting apneas/hypopneas. To increase the accuracy of apnea/hypopnea detection, an accurate airflow estimation is required. However, current airflow measurement techniques employed in portable devices are inconvenient and subject to displacement during sleep. In this study, algorithms were developed to estimate respiratory motion and airflow using tracheo-sternal motion and tracheal sounds.
Patients and Methods
Adults referred for polysomnography were included. Simultaneous to polysomnography, a patch device with an embedded 3-dimensional accelerometer and microphone was affixed to the suprasternal notch to record tracheo-sternal motion and tracheal sounds, respectively. Tracheo-sternal motion was used to train two mathematical models for estimating changes in respiratory motion and airflow compared to simultaneously measured thoracoabdominal motion and nasal pressure from polysomnography. The amplitude of the estimated airflow was then adjusted by the tracheal sound envelope in segments with unstable breathing.
Results
Two hundred and fifty-two subjects participated in this study. Overall, the algorithms provided highly accurate estimates of changes in respiratory motion and airflow with mean square errors (MSE) of 3.58 ± 0.82% and 2.82 ± 0.71%, respectively, compared to polysomnographic signals. The estimated motion and airflow from the patch signals detected apneas and hypopneas scored on polysomnography in 63.9% and 88.3% of cases, respectively.
Conclusion
This study presents algorithms to accurately estimate changes in respiratory motion and airflow, which provides the ability to detect respiratory events during sleep. Our study suggests that such a simple and convenient method could be used for portable monitoring to detect sleep apnea. Further studies will be required to test this possibility.
Abbreviations
SA, sleep apnea; UA, upper airway; OSA, obstructive sleep apnea; CSA, central sleep apnea; AHI, apnea/hypopnea index; PSG, polysomnography; BMI, body mass index; UHN, University Health Network; RIP, respiratory inductance plethysmography; SaO2, arterial oxyhemoglobin saturation; AASM, American Academy of Sleep Medicine; PELT, pruned exact linear time; MSE, mean square error; ANOVA, analysis of variance.
Disclosure
Dr. Nasim Montazeri Ghahjaverestan reports personal fees from Bresotec Inc., during the conduct of the study. Dr. Cristiano Aguiar reports a Provisional patent pending to Bresotec Inc. Mr Jackson Yu reports a Provisional patent pending to Bresotec Inc. Dr. T. Douglas Bradley reports personal fees from Bresotec Inc., during the conduct of the study.