Analysis of cardiovascular oscillations using nonlinear dynamics methods for an enhanced diagnosis of heart and neurological diseases and for risk stratification

Analyses of cardiovascular oscillations provided new insights into cardiovascular variability changes (as e.g. heart rate and blood pressure variability) under various physiological and pathological conditions and lead to additional prognostic information about a patient's outcome. Chronic heart failure (CHF) is a major and growing public health concern affecting about 23 million people worldwide. More than 1 million people die every year due to CHF. Many such victims could have probably survived if this disease had been diagnosed at an early stage and if the individual risk would have been known before starting an optimal therapy on time. Nonlinear dynamics (NLD) methods have shown new insights into heart rate (HR) variability changes and complement traditional time-and frequency domain analyses. Some of the most prominent indices of nonlinear and fractal dynamics are briefly introduced as well as their algorithmic implementations and applications in clinical trials as risk stratification in CHF patients. A classification of autonomic nervous system dysfunctions caused by neurological diseases and genetic influences are also discussed. Several of the nonlinear indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multi-fractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in analyzing cardiovascular variability are expected when applying multidimensional and multivariate approaches.