Assessment of Traditional and AI-based Cardiorespiratory Coupling Indicators for Spontaneous and Controlled Breathing

We developed a framework for the robust estimation of cardiorespiratory coupling. First, we re-examined the existing tools using a cardiorespiratory coupling model. We then suggested a model-derived approach and modified it by considering the features of physiological signals. Our focus was on respiratory variability in the cardiorespiratory coupling assessment. The AI-based tool was also applied to clarify the impact of breathing irregularities. The developed framework may enhance clinical assessments and allow personalised monitoring of cardiorespiratory coupling for early diagnosis or characterisation of physiological states.