Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea

Abstract

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.

Keywords: Airflow; Biomedical signal processing; Blood oxygen saturation; Childhood Adenotonsillectomy Trial; Classification; Electrocardiogram; Machine learning; Regression; Sleep Heart Health Study; Sleep apnea.