Standard parameters of heart rate variability are restricted in measuring linear effects, whereas nonlinear descriptions often suffer from the curse of dimensionality. An approach which might be capable of assessing complex properties is the calculation of entropy measures from normalised periodograms. Two concepts, both based on autoregressive spectral estimations are introduced here. To test the hypothesis that these entropy measures may improve the result of high risk stratification, they were applied to a clinical pilot study and to the data of patients with different cardiac diseases. The study shows that the entropy measures discussed here are useful tools to estimate the individual risk of patients suffering from heart failure. Further, the results demonstrate that the combination of different heart rate variability parameters leads to a better classification of cardiac diseases than single parameters.