The early detection and treatment of sleep apnea syndrome (SAS) is becoming an important issue in medical practice, as the conventional polysomnography-based method is not only costly, but also demands high expertise from technicians. In this study, we propose a method for diagnosing SAS using noninvasive measurements of oxygen saturation (SpO2), obtained from a pulse oximeter as the input signal, using convolutional neural networks (CNNs). As a result of 10-fold cross-validation, we confirmed that the proposed method yields an 88.99% accuracy.
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| http://dx.doi.org/10.1109/EMBC53108.2024.10782933 | DOI Listing |
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