Taniguchi Nobuhito, Kawaguchi Hiroshi, Nagano Tatsuya, Izumi Shintaro
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782933.
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.
睡眠呼吸暂停综合征（SAS）的早期检测与治疗正成为医学实践中的一个重要问题，因为传统的基于多导睡眠图的方法不仅成本高昂，而且对技术人员的专业要求也很高。在本研究中，我们提出了一种利用从脉搏血氧仪获取的氧饱和度（SpO2）无创测量值作为输入信号，通过卷积神经网络（CNN）诊断SAS的方法。经过十折交叉验证，我们证实所提出的方法准确率达到88.99%。
