Multiscale Entropy Analysis of Heart Rate Variability for Risk Stratification of Cardiovascular Events in Hypertensive Patients

Prediction of the occurrence of a fatal cardiovascular event in hypertensive patients is a clinically interesting challenge, because this information could be helpful to reduce large rates of mortality and morbidity associated with high blood pressure. Recently, entropy analysis of the heart rate variability (HRV) extracted from the electrocardiogram (ECG) recording has reported a promising predictive ability in that context. However, this kind of analysis only assesses a single temporal scale, discarding more complex dynamics. Hence, the main goal of the present work is to extend such an entropy analysis to a multiscale domain and analyze potential improvement in the prediction of hypertensive patients with high cardiovascular risk. The results obtained with a multiscale entropy algorithm especially designed for dealing with short-time physiological signals have provided between 3 and 9% superior ability than conventional entropy when discerning among hypertensive patients who suffered and did not suffer from a cardiovascular event during a follow-up of one year. Hence, the multiscale entropy analysis seems to reveal novel insights in HRV dynamics of hypertensive patients.