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http://dx.doi.org/10.9718/JBER.2016.37.4.127

Classification of Sleep/Wakefulness using Nasal Pressure for Patients with Sleep-disordered Breathing  

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)
Publication Information
Journal of Biomedical Engineering Research / v.37, no.4, 2016 , pp. 127-133 More about this Journal
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.
Keywords
Sleep; Wakefulness; Nasal pressure; Sleep-disordered breathing; Continuous positive airway pressure (CPAP);
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1 N. Collop, "The effect of obstructive sleep apnea on chronic medical disorders," Cleve. Clin. J. Med., vol. 74, no. 1, pp. 72-78, 2007.   DOI
2 B. Prasad, et al. "Continuous positive airway pressure device-based automated detection of obstructive sleep apnea compared to standard laboratory polysomnography," Sleep Breath., vol. 14, no. 2, pp. 101-107, 2010.   DOI
3 N. Wolkove, et al. "Long-term compliance with continuous positive airway pressure in patients with obstructive sleep apnea," Can. Resp. J., vol. 15, no. 7, pp. 365-369, 2008.
4 C.A Kushida, et al. "Practice parameters for the indications for polysomnography and related procedures: an update for 2005," SLEEP, vol. 28, no. 4, pp. 499-521, 2005.   DOI
5 S. Su, et al. "A comparison of polysomnography and a portable home sleep study in the diagnosis of obstructive sleep apnea syndrome," J. Otolaryngol. Head Neck Surg., vol. 131, no. 6, pp. 844-850, 2004.   DOI
6 J. Hedner, et al. "A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients," SLEEP, vol. 27, no. 8, pp. 1560-1566, 2004.   DOI
7 M.O. Mendez, et al. "Sleep staging from heart rate variability: time-varying spectral features and hidden Markov models," Int. J. of Biomed. Eng. Technol., vol. 3, no. 3-4, pp. 246-263, 2010.   DOI
8 D.F. Kripke, et al. "Wrist actigraphic scoring for sleep laboratory patients: algorithm development," J. sleep res., vol. 19, no. 4, pp. 612-619, 2010.   DOI
9 J. Hedner, et al. "Sleep staging based on autonomic signals: a multi-center validation study," J. Clin. Sleep Med., vol. 7, no. 3, pp. 301-306, 2011.
10 X. Long, et al. "Sleep and wake classification with actigraphy and respiratory effort using dynamic warping," IEEE J. Biomed. Health Inform., vol. 18, no. 4, pp. 1272-1284, 2014.   DOI
11 G. Guilleminault, et al. "The sleep apnea syndromes," Annu. Rev. Med., vol. 27, no. 1, pp. 465-484, 1976.   DOI
12 H.K. Lee, et al. "Nasal pressure recordings for automatic snoring detection," Med. Biol. Eng. Comput., vol. 53, no. 11, pp. 1103-1111, 2015.   DOI
13 H.K. Lee, Automatic Sleep-Disordered Breathing Detection Using a Single Channel Record in Patients with Sleep Apnea Hypopnea Syndrome, Ph.D. Dissertation, The Graduate School Yonsei University, Seoul (2013).
14 R.B. Berry, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, American Academy of Sleep Medicine, 2007.
15 H.S. Shin, et al., "Adaptive threshold method for the peak detection of photoplethysmographic waveform," Comput. Biol. Med., vol. 39, no. 12, pp. 1145-1152, 2009.   DOI
16 A. Petrie, et al. Medical statistics at a glance, John Wiley & Sons, 2009.
17 R.O. Duda, et al. Pattern classification, 2nd Edition, Willey-Interscience, 2001.
18 S.J. Redmond, et al. "Sleep staging using cardiorespiratory signals," Somnologie., vo1. 1, no. 4, pp. 245-256, 2007.
19 T. Willemen, et al. "An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification," IEEE J. Biomed. Health Inform., vol. 18, no.2, pp. 661-669, 2014.   DOI