Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain.
Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
Adv Exp Med Biol. 2022;1384:131-146. doi: 10.1007/978-3-031-06413-5_8.
The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.
整夜多导睡眠图显示出一系列诊断阻塞性睡眠呼吸暂停（OSA）的缺陷，这导致了对基于人工智能的替代方法的研究。为此，已经评估了许多经典的机器学习方法。在本章中，我们展示了在科学文献中找到的主要方法，以及用于开发模型的最常用的数据、有用的大型且易于获取的数据库，以及评估性能的合适方法。此外，还展示了一些选定研究的结果作为这些方法的示例。无论采用何种方法，这些结果均报告了非常高的诊断性能。这使我们得出结论，传统的机器学习方法是开发新的 OSA 诊断简化方案的有用技术，并可作为深度学习等其他更现代方法的基准。
