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KMID : 1137820160370040127
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의공학회지
2016 Volume.37 No. 4 p.127 ~ p.133
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Classification of Sleep/Wakefulness using Nasal Pressure for Patients with Sleep-disordered Breathing
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Park Jong-Uk
Jeoung Pil-Soo
Kang Kyu-Min
Lee Kyoung-Joung
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Abstract
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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, SDLEN (MD = 0.84, p < 0.01), PHF (MD = 0.81, p < 0.01), SDAMP (MD = 0.76, p = 0.031) and MEANAMP (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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KEYWORD
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Sleep, Wakefulness, Nasal pressure, Sleep-disordered breathing, Continuous positive airway pressure (CPAP)
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