Respiratory Motion and Airflow Estimation During Sleep Using Tracheal Movement and Sound

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.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

“…Previous studies have demonstrated the potential of using breathing sounds to develop predictive models for OSA detection. However, many of these studies validated their prediction models using sound collected in controlled environments, referencing level 1 PSG . Furthermore, previous studies failed to account for home environment noises, which are essential for accurate OSA prediction in a home setting .…”

“…Sound-based assessment for OSA has emerged as a promising approach that enables noncontact and in-home monitoring using smartphones or wearable devices . Previous studies have demonstrated the potential of using breathing sounds to develop predictive models for OSA detection.…”

“…Sound-based assessment for OSA has emerged as a promising approach that enables noncontact and in-home monitoring using smartphones or wearable devices. [8][9][10] Previous studies have demonstrated the potential of using breathing sounds to develop predictive models for OSA detection. However, many of these studies validated their prediction models using sound collected in controlled environments, referencing level 1 PSG.…”

“…However, many of these studies validated their prediction models using sound collected in controlled environments, referencing level 1 PSG. 8,[10][11][12] Furthermore, previous studies failed to account for home environment noises, which are essential for accurate OSA prediction in a home setting. 10,12 To address this, our predeveloped model was trained using data that incorporated various home noises, ensuring its ability to accurately predict respiratory events in such environments.…”

“…8,[10][11][12] Furthermore, previous studies failed to account for home environment noises, which are essential for accurate OSA prediction in a home setting. 10,12 To address this, our predeveloped model was trained using data that incorporated various home noises, ensuring its ability to accurately predict respiratory events in such environments. However, it is important to note that the PSG data used in our previous studies were obtained in a sleep laboratory, which may not fully represent the home environment.…”