Sleep/Wakefulness Detection Using Tracheal Sounds and Movements

Overview

Journal Nat Sci Sleep

Publisher Dove Medical Press

Date 2020 Nov 25

PMID 33235534

Citations 7

Authors

Nasim Montazeri Ghahjaverestan

Sina Akbarian

Maziar Hafezi

Shumit Saha

Kaiyin Zhu

Bojan Gavrilovic

Babak Taati

Azadeh Yadollahi

Affiliations

Soon will be listed here.

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 (<0.001) and 0.70 (<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.

Citing Articles

Unveiling the Impact of Respiratory Event-Related Hypoxia on Heart Sound Intensity During Sleep Using Novel Wearable Technology.

Kabir M, Assadi A, Saha S, Gavrilovic B, Zhu K, Mak S Nat Sci Sleep. 2024; 16:1623-1636.

PMID: 39430234 PMC: 11491078. DOI: 10.2147/NSS.S480687.

Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis.

Domingues D, Rocha P, Miachon A, Giampa S, Soares F, Genta P Sci Rep. 2024; 14(1):24562.

PMID: 39427062 PMC: 11490485. DOI: 10.1038/s41598-024-75935-8.

Sleep Apnea Detection by Tracheal Motion and Sound, and Oximetry via Application of Deep Neural Networks.

Montazeri Ghahjaverestan N, Aguiar C, Hummel R, Cao X, Yu J, Bradley T Nat Sci Sleep. 2023; 15:423-432.

PMID: 37274453 PMC: 10239247. DOI: 10.2147/NSS.S397196.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

Tran H, Hong J, Jang H, Jung J, Kim J, Hong J J Med Internet Res. 2023; 25:e46216.

PMID: 37261889 PMC: 10273036. DOI: 10.2196/46216.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

Hong J, Tran H, Jung J, Jang H, Lee D, Yoon I Nat Sci Sleep. 2022; 14:1187-1201.

PMID: 35783665 PMC: 9241996. DOI: 10.2147/NSS.S361270.

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