Scaling Exponents in Heart Rate Variability
Long recordings of heart rate variability (HRV) display non-stationary characteristics and exhibit long- and short-range correlations. The nonparametric methodology detrended fluctuation analysis (DFA) has become a widely used technique for the detection of long-range correlations in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) modelling. These models are an extension of the AR models usual in HRV analysis and have special interest for applications because of their ability for modelling both short- and long-term behaviour of a time series. In this work, DFA is used to assess also short-range scales, further characterizing the data. The methods are applied to 24 h HRV recordings from the Noltisalis database, collected from healthy subjects, patients suffering from congestive heart failure and heart transplanted patients. The analysis of short-range scales leads to a better discrimination between the different groups.
Research funded by FEDER through the programme COMPETE and by the Portuguese Government through the FCT—Fundação para a Ciência e a Tecnologia—under the projects PEst-C/MAT/UI0144/2011, PEst-C/MAT/UI4106/2011, and PEst-OE/MAT/UI4080/2011.
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