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.

Keywords: respiratory airflow; sleep apnea; tracheal acoustics.

Figure 1

An overview of data analysis. (A) Respiratory motion estimation. (B) Airflow estimation. Dashed arrows indicate the stream of training set. (C) The Patch is attached over the suprasternal notch and its data collection unit (hub) is placed on a bed-side table.

Figure 2

Representative traces of the predictors extracted from the tracheal signals and the estimated motion and airflow in comparison to the reference signals extracted from the PSG during segments with intermittent (A) obstructive and (B) central apneas. The shaded regions highlight the occurrence of apneas. In the estimated airflow panel, the thin black and thick grey lines demonstrate the estimated airflow with and without modulation by the sound envelope, respectively.

Figure 3

The average value of mean squared error (MSE,%) and the repeated measure correlation with related confidence interval calculated for the subjects in the test set to compare the estimated motion (A and B) and airflow (C and D) signals to the related reference signals during apneas, hypopneas and normal breathing during sleep and wakefulness. *p ≤ 0.001.

Figure 4

The average value of mean squared error (MSE,%) between estimated and reference motion (A and C) and airflow (B and D) signals calculated for subjects in the test set for different sex and body mass index (BMI) (Overweight: BMI ≥25, Non-overweight: BMI <25) categories, respectively.

Figure 5

The average level of the estimated motion (A) and airflow (B) at baseline right before the onset of the events, and during hypopneas and apneas normalized as a percentage of the average value during a 5-minute period of normal breathing during wakefulness in the supine position prior to lights out. (C) For both apneas and hypopneas, there is a greater reduction in the average normalized level of estimated motion for central than for obstructive events. *Significant with p-value ≤0.001.