| dc.description.abstract |
Sleep apnea is a potentially severe sleep disorder, which occurs when a person ’s breathing is disrupted during sleep. Polysomnography (PSG) is the standard approach for diagnosing sleep apnea. However, this test is quite obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. This diagnostic process may be simplified by applying machine learning techniques on data generated by wearable devices with pulse oximetry (SpO2) and heart rate sensors. Feature selection is an important step in the training of machine learning models, which reduces the number of features required to train the machine learning models and classify new query instances. Correlation between features is an indication of how one feature impacts another. In this paper, we study the correlation between SpO2 and heart rate in the detection of sleep apnea. We employ Pearson’s correlation |
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