Analysis of crossovers in the interbeat sequences of elderly individuals and heart failure patients

Many physical and biological systems exhibit complex behavior characterized by long-range power-law correlations. Detrended fluctuation analysis (DFA) is a scaling analysis method that provides a scaling parameter to represent the correlation properties of a signal. The study of interbeat sequences with the DFA method has revealed the presence of crossovers associated with physiological aging and heart with failure; the hinges present in the crossover region from both the elderly healthy individuals and the patients with congestive heart failure (CHF) are in opposite directions. The interbeat sequences of healthy young persons do not show crossovers. In this paper we study interbeat time series of healthy young and elderly persons and patients with CHF. We use the DFA-m method, where m refers to the order of the polynomial function used for the fitting. For instance, DFA-2 filters linear trends and DFA-3 filters quadratic trends. We found that the presence of the crossovers and the direction of the hinges are conserved when we apply the DFA method for different values of m. Therefore we conclude that the DFA-m method is a reliable method to accurately quantify correlations in interbeat time series even if there are polynomial trends. We can characterize the crossovers and we can conclude that the crossovers are not a result of the trends; they are part of the system dynamics.