Abstract
Chronic heart failure (CHF) is a major and growing public health concern (∼23 million people worldwide) with five-year survival rates of 25% in men and 38% in women. Objective of this study was to investigate whether linear and nonlinear heart rate variability (HRV) indices enhance risk prediction in patients with CHF. To discriminate between low risk (stable condition, N=459) and high risk (cardiac death, N=50) CHF patient groups, nonlinear indices from compression entropy (CE), detrended fluctuation analysis (DFA), symbolic dynamics (SD) and standard linear HRV analysis were calculated from 24h Holter ECG recordings. Indices from nonlinear dynamics (CE, DFA, SD: p≪0.001) contribute together with clinical parameters NYHA and LVEF to an enhanced risk stratification in CHF patients.
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