清醒
阻塞性睡眠呼吸暂停
多导睡眠图
医学
呼吸暂停
持续气道正压
气道
睡眠呼吸暂停
机器学习
随机森林
人工智能
计算机科学
物理医学与康复
脑电图
麻醉
精神科
作者
Jan Lim,Shehroz S. Khan,Aditya Pandya,Clodagh M. Ryan,Ahmed Haleem,Niveca Sivakulam,Hosna Sahak,Adnan Ul Haq,Kori E. Macarthur,Hisham Alshaer
标识
DOI:10.1109/embc.2019.8856754
摘要
Background and Rational: Obstructive Sleep Apnea (OSA) is a common disorder, affecting almost 10% of adults, but very underdiagnosed. This is largely due to limited access to overnight sleep testing using polysomnography (PSG). Our goal was to distinguish OSA from healthy individual using a simple maneuver during wakefulness in combination with machine learning methods. Methods: Participants have undergone an overnight PSG to determine their ground truth OSA severity. Separately, they were asked to breathe through a nasal mask or a mouth piece through which negative pressure (NP) was applied, during wakefulness. Airflow waveforms were acquired and several features were extracted and used to train various classifiers to predict OSA. Results and Discussion: The performance of each classifier and experimental setup was calculated. The best results were obtained using Random Forest classifier for distinguishing OSA from healthy individuals with a very good area under the curve of 0.80. To the best of our knowledge, this is the first study to deploy machine learning and NP with promising path to diagnose OSA during wakefulness.
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