Sleep Stage Detection Using Tracheal Breathing Sounds: A Pilot Study

Abstract

Sleep stage detection is needed in many sleep studies and clinical assessments. Generally, sleep stages are identified using spectral analysis of electrocephologram (EEG) and electrooculogram (EOG) signals. This study, for the first time, has investigated the feasibility of detecting sleep stages using tracheal breathing sounds, and whether the change of breathing sounds due to sleeping stage differs at different periods of sleeping time; the motivation was seeking an alternative technique for sleep stage identification. The tracheal breathing sounds of 12 individuals, who were referred for full overnight polysomnography (PSG) assessment, were recorded using a microphone placed over the suprasternal notch, and analyzed using higher order statistical analysis. Five noise-and-snore-free breathing cycles from wakefulness, REM and Stage II of sleep were selected from each subject for analysis. Data of the REM and Stage II were selected from beginning, middle and close to end of sleeping time. Hurst exponent was calculated from the bispectra of the inspiratory sounds of each subject at each sleeping stage in different periods of sleeping time. The participants' sleep stage were determined by sleep lab technologists during the PSG study using EEG and EOG signals. The results show separate and non-overlapping clusters for wakefulness, REM and Stage II for each subject. Thus, using a simple linear classifier, we were able to classify REM and Stage II of each subject with 100% accuracy. In addition, the results show that the same pattern existed as long as the REM and Stage II segments were close (less than 3 h) to each other in terms of time.

<p>Sleep/Wakefulness Detection Using Tracheal Sounds and Movements</p>

“…Therefore, in this study, the Hurst component was calculated from the autocorrelation of sound energy. In-line with the findings in Soltanzadeh and Moussavi, 7 the Hurst exponent was higher during sleep indicating a more regular pattern of breathing compared to wakefulness. However, unlike the finding in Soltanzadeh and Moussavi, 7 no significant difference was found between REM and NREM of full-night data.…”

“…In previous studies, sleep stages were detected overnight by Dafna et al 5,6 using an ambient microphone and in a few short segments of sleep using tracheal sound by Soltanzadeh and Moussavi. 7 In this study, significant changes were found between REM and other states only in sound features. More in-depth analysis to differentiate various sleep stages should be addressed in future studies.…”

“…Moreover, from the calculated autocorrelation of tracheal sounds, the Hurst exponent was extracted. In a study by Soltanzadeh and Moussavi, 7 the Hurst exponent of the bispectrum of tracheal sound was found to be higher across sleep stages than during wakefulness and can be used for differentiating sleep from wakefulness with 100% accuracy. However, their study was applied on a limited number of segments and its performance was not assessed on overnight data or on sleep apnea population.…”

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks

“…However, both PSG and H-PSG tend to involve bulky equipment that requires significant time to put on properly and to interpret; the systems tend to be cumbersome, and might themselves interact with the sleep behavior, which make them impractical for long-term sleep/wake detection. More recent methods involve less cumbersome equipment such as using a smartphone placed on the bed to track movement via accelerometers [14], using contact [15] and non-contact microphones [16], short-range doppler radar [17] and WiFi [18].…”

Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement

“…Most of these methods use heart rate variability (HRV) [3 -5] or electrocardiogram (ECG)-derived respiration in addition to the HRV features [6] from an ECG. Other studies used peripheral arterial tone (PAT) [7], an EOG [8], tracheal breathing sound [9], or respiratory signals [10] to detect the sleep stages. Although the sleep stages could be detected using these methods (with average accuracies ranging from 56.0 % to 81.7 %), attachment of the sensor to measure the physiological signal during sleep still causes a great deal of inconvenience to the sleeper.…”