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Sleep apnea (SA) is a common sleep disorder that is not easy to detect. Recent studies have highlighted ECG analysis as an effective method of diagnosing SA. Because the changes caused by SA on the ECG are imperceptible, the need for new methods in diagnosing this disease is required more than ever. Machine Learning (ML) is recognized as one of the most successful methods of computer aided diagnosis. ML uses new methods to diagnose diseases using past clinical results. The purpose of this study is to evaluate studies using ML algorithms based on ECG characteristics to assess people suffering from SA. In this study, systematically-reviewed articles written in English before October 2020 and indexed in PubMed, Scopus, Web of Science, and IEEE databases were searched with no lower time limit. From these articles, 48 were selected for further review. The selected articles adopteddifferent ML …