Quantifying cardio-pulmonary correlations using the cross-wavelet transform: Validating a correlative method

Dynamic changes in physiologic systems have become an increasingly important topic in biomedical signal processing. To demonstrate a novel approach to this type of analysis, we recorded the cardiac and respiratory rhythms of six normal subjects over 5 minutes. We used cross-wavelet transforms to identify any correlations between these signals and then a unique approach to surrogate data generation in the frequency domain to confirm the statistical significance of the correlations that were found. The cross-wavelet transform provides a means of statistically quantifying weak correlations between systems that might otherwise not show significant interactions, while the method of surrogate data generation is a robust way of confirming a true physiological relationship.