Are “scaling patterns” useful tools for exploring fractality in heart rate variability data?

Detrended fluctuation analysis (DFA) is becoming a widely used technique for exploring the structure of correlations in heart rate variability (HRV) data. This method provides a scaling or fractal exponent α derived from the behaviour of the root-mean-square fluctuations along different time scales n. Rather than just finding a single exponent, covering either short or long range, we recently suggested to track the local evolution of α as in this way scaling patterns (SP), which seem to provide more detailed characterisations of HRV data, are revealed. Here, we evaluate such potential advantage by classifying long-term data from 50 subjects in normal sinus rhythm and 29 congestive heart failure patients. Using the SP we achieved a significantly better classification of these data than using α, thereby confirming that the SP provide a useful assessment of the correlation structure in HRV data.