Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Classification of Sleep/Wakefulness using Nasal Pressure for Patients with Sleep-disordered Breathing

비강압력신호를 이용한 수면호흡장애 환자의 수면/각성 분류

  • Park, Jong-Uk (Department of Biomedical Engineering, College of Health science, Yonsei University) ;
  • Jeoung, Pil-Soo (Department of Biomedical Engineering, College of Health science, Yonsei University) ;
  • Kang, Kyu-Min (Department of Biomedical Engineering, College of Health science, Yonsei University) ;
  • Lee, Kyoung-Joung (Department of Biomedical Engineering, College of Health science, Yonsei University)
  • 박종욱 (연세대학교 보건과학대학 의공학부) ;
  • 정필수 (연세대학교 보건과학대학 의공학부) ;
  • 강규민 (연세대학교 보건과학대학 의공학부) ;
  • 이경중 (연세대학교 보건과학대학 의공학부)
  • Received : 2016.05.22
  • Accepted : 2016.08.12
  • Published : 2016.08.31
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Abstract

This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.

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References

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