Heart rate variability is a relevant predictor of cardiovascular risk in humans. However, to use heart and blood pressure (BP) variability or baroreflex sensitivity as markers for hypertensive pregnancy disorders, it is first necessary to describe these parameters in normal pregnancy. To accommodate the complexities of autonomic cardiovascular control we added parameter domains of nonlinear dynamics to conventional linear methods of time and frequency domains. The BP of 27 women with normal pregnancy and 14 nonpregnant women were monitored at a high resolution (200 Hz sampling frequency) using a Portapres for 30 min. The pregnant women were divided into groups of 32 or less or greater than 32 weeks of gestation. Pregnant and nonpregnant women were classified into subclasses of maternal age of less than 28 or 28 or more years. Except for two single parameter domains, we found no significant differences in heart rate and BP variability for pregnant women with different gestational age or different maternal age. Moreover, no significant differences in spontaneous baroreflex sensitivity could be found between pregnant women regardless of either their age or gestational age. In contrast, all measures of nonlinear dynamics of heart rate variability as well as all parameter domains of spontaneous baroreflex sensitivity showed significant changes between pregnant and nonpregnant women, whereas BP variability did not differ between those groups. This complex assessment of autonomic cardiovascular regulation has shown that the parameters tested are stable in the second half of normal pregnancy, and might have the potential to be excellent indicators of pathophysiologic conditions.

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Figure 4. Postextrasystolic regulation patterns (mean deviation from reference for each group) of the systolic BP (SBP, grey line) and the heart rate (HR, black line) in controls (top) and IDC patients (bottom) In controls, a typical baroreflex response was apparent, whereas in IDC patients, the baroreflex response was abolished due to the pronounced postextrasystolic potentiation (PESP).

In this study heart rate variability (HRV) analysis was applied to characterize patients suffering from coronary heart disease (CHD), dilated cardiomyopathy (DCM) and patients who had survived an acute myocardial infarction (MI). On the basis of several HRV parameters, an optimal discrimination between the different kinds of cardiovascular diseases and between the diseases and healthy controls (HC) was derived by feature selection and linear classification. For each task a small favourable subset of a set of 33 potentially interesting HRV measures was selected with the intention of improving the diagnostic value and facilitating the physiological interpretation of HRV analysis. Time- and frequency-domain parameters as well as parameters from non-linear dynamics were included in the analysis. With the expectation that different diseases are characterized by different phenomena, feature selection was applied for each task separately. Using the features optimal for one task to another task can reveal a loss in performance, but it turned out that one specific parameter set (set1: normalized low frequency LF/P and a non-linear variability measure WPSUM13) was applicable for all tasks, where diseased and healthy subjects have to be distinguished, without significant reduction in performance. This set seems to be a general marker for pathologic changes in HRV and might be used for early detection of heart diseases. The classification between different heart diseases requires another parameter set (set2: meanNN and sdaNN, reflecting the steady state behaviour of the heart rate and long and short term SEAR describing the spectral composition). However, the use of set1 for the separation of different kinds of diseases, where set2 is appropriate, led to significant reduction in performance and vice versa. This observation may be important for future developments of HRV measures especially suitable for the assessment of disease severity.

Abstract. 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.