Sleep/Wakefulness Detection Using Tracheal Sounds and Movements

Abstract

Purpose

The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements.

Materials and Methods

Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography.

Results

Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3±8.66% with a sensitivity of 87.8±10.8 % (sleep), specificity of 71.4±18.5% (awake), F1 of 88.1±9.3% and Cohen’s kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p<0.001) and 0.70 (p<0.001), respectively.

Conclusion

Sleep/wakefulness periods can be detected using tracheal sound and movements. The results of this study combined with our previous studies on screening sleep apnea with tracheal sounds provide strong evidence that respiratory sounds analysis can be used to develop robust, convenient and cost-effective portable devices for sleep apnea monitoring.

Keywords:

• sleep apnea
• apnea/hypopnea index
• principal component analysis
• classification
• imbalanced data

Acknowledgments

This study was supported by FedDev Ontario, Ontario Centres of Excellence (OCE), NSERC Discovery grant, and BresoTEC Inc. Toronto, ON, Canada.

Disclosure

Dr Babak Taati reports grants from FedDev Ontario, and BresoTec Inc. Dr Azadeh Yadollahi reports financial support by operating grants from NSERC(RGPIN-2016-06549); and Ontario Centers of Excellence-VIPII Project#25510. The authors report no other conflicts of interests in this work.