Sleep Stage Detection Using Tracheal Breathing Sounds: A Pilot Study

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• 作者：Ramin Soltanzadeh ; Zahra Moussavi
• 关键词：Sleep stage ; Respiratory sounds ; Bispectral analysis ; Hurst exponent
• 刊名：Annals of Biomedical Engineering
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：43
• 期：10
• 页码：2530-2537
• 全文大小：1,281 KB
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• 作者单位：Ramin Soltanzadeh (1)
  Zahra Moussavi (1)
  
  1. Biomedical Engineering Program, University of Manitoba, 75 Chancellor Circle, Winnipeg, MB, R3T 5V6, Canada
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Biomedicine
  Biomedical Engineering
  Biophysics and Biomedical Physics
  Mechanics
  Biochemistry
  
• 出版者：Springer Netherlands
• ISSN：1573-9686

文摘

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. Keywords Sleep stage Respiratory sounds Bispectral analysis Hurst exponent