Nature and Science of Sleep (Jul 2022)
Respiratory Motion and Airflow Estimation During Sleep Using Tracheal Movement and Sound
Abstract
Nasim Montazeri Ghahjaverestan,1,2 Wei Fan,3 Cristiano Aguiar,3 Jackson Yu,3 T Douglas Bradley1,3– 5 1Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada; 2Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; 3Bresotec Inc., Toronto, Ontario, Canada; 4Department of Medicine of the University of Toronto, Toronto, Ontario, Canada; 5Department of Medicine of the University Health Network Toronto General Hospital, Toronto, Ontario, CanadaCorrespondence: T Douglas Bradley, Department of Medicine of the University Health Network Toronto General Hospital, Room 9N-943, 200 Elizabeth Street, Toronto, Ontario, M5G 2C4, Canada, Tel +1 416-340-4719, Fax +1 416-340-4197, Email [email protected]: 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.Keywords: respiratory airflow, sleep apnea, tracheal acoustics
