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.
Petrock
Anne Marie
AM
New Jersey Neuroscience Institute Research, JFK Medical Center, Edison, NJ 08818, USA. ampetrock@solarishs.org
Donnelly
Diane L
DL
Rosenberg
Michael L
ML
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D005583:Fourier Analysis; D006321:Heart / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008168:Lung / Q000503:physiopathology*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012119:Respiration; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors