A Scoping Review of Current Methodologies of Measuring Cardiopulmonary Coupling and their Limitations in Sleep Apnea Detection

Abstract

Cardiopulmonary coupling (CPC) analysis is a noninvasive technique increasingly used to evaluate the autonomic nervous system's control over heart and respiratory functions, particularly during sleep. This review delves into current methods
of CPC analysis, including the preprocessing of electrocardiogram (ECG) signals, extraction of meaningful measures, and features,
and the use of machine-learning classification for sleep-related diseases. The review also examines different patient cohorts in the
existing literature and the efficiency and accuracy of automated sleep apnea detection, highlighting the limitations and potential of these methodologies in clinical settings.