Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has a high clinical relevance in pediatrics, as it can negatively impact the physical and mental health of children. Since mouth breathing is an anomalous condition in the general population with only 2% prevalence in our data set, we are facing an anomaly detection problem. This type of human medical data is commonly approached with deep learning methods. However, applying multiple supervised and unsupervised machine learning methods to this anomaly detection problem showed that classic machine learning methods should also be taken into account. This paper compared deep learning and classic machine learning methods on respiratory data during sleep using a leave-one-out cross validation. This way we observed the uncertainty of the models and their performance across participants with varying signal quality and prevalence of mouth breathing. The main contribution is identifying the model with the highest clinical relevance to facilitate the diagnosis of chronic mouth breathing, which may allow more affected children to receive appropriate treatment.

以可靠、非侵入性的方式识别睡眠期间的口呼吸具有挑战性，目前尚未纳入睡眠研究中。然而，它在儿科中具有很高的临床相关性，因为它会对儿童的身心健康产生负面影响。由于口呼吸在普通人群中是一种异常情况，在我们的数据集中只有 2% 的患病率，因此我们面临着异常检测问题。此类人类医疗数据通常采用深度学习方法来处理。然而，将多种监督和无监督机器学习方法应用于这个异常检测问题表明，经典的机器学习方法也应该被考虑在内。本文使用留一法交叉验证，对睡眠期间呼吸数据的深度学习和经典机器学习方法进行了比较。通过这种方式，我们观察了模型的不确定性及其在不同信号质量和口呼吸流行率的参与者中的表现。主要贡献是确定具有最高临床相关性的模型，以促进慢性口呼吸的诊断，这可能使更多受影响的儿童接受适当的治疗。