Multiscale complexity analysis of heart rate dynamics in heart failure: Preliminary findings from the music study

Recently, a new complexity measure, multiscale entropy (MSE), has been developed based on the quantification of heart rate fluctuations over a range of time scales. Here, we use the MSE algorithm to analyze the cardiac interbeat interval time series from patients with congestive heart failure (CHF) enrolled in the MUSIC study. Our hypothesis is that the heart rate time series from the patients who survived have more dynamical complexity that those from patients who did not survive. MUSIC (Muerte Subita en Insufficiencia Cardiaca) is a prospective multicenter longitudinal study designed to assess risk predictors of death inpatients with heart failure. The MSE algorithm was used to quantify the degree of complexity of the interbeat interval time series derived from 24-hour Hotter recordings. The analysis was performed up to scale 20 that corresponds to approximately 20 seconds. For all measured time scales, the mean MSE values were significantly (p < 0.01) higher for the entire RR time series from the group of patients who survived than for the time series from the group of non-survivors. Similar results were obtained from the analysis of the time series of consecutive sinus (NN) beats. These findings indicate that the heart rate dynamics of survivors are more complex than those of non-survivors, and suggest that MSE analysis may be useful in risk stratification of patients with mild-moderate symptoms of CHF.