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Heart Rate Variability (HRV), i. e., the variation of time intervals between consecutive heart beats, is a marker of the health status, since it unveils changes in beat-to-beat variation of the heart, even before there is a remarkable change in heart rate itself. HRV reflects the balance between the sympathetic and the parasympathetic nervous system. The heart rate itself is nonstationary and the structure generating the signal involves nonlinear contributions. Thus, nonlinear methods to quantify the variability of the heart rate gained interest over the last years. In this work, two nonlinear indices, i. e., Correlation Dimension (CD) and Fractal Dimension (FD), to quantify HRV derived from mathematical models are presented. The implemented methods are tested on their ability to differentiate between healthy and pathological subjects. The databases used for the test are retrieved from PhysioNet. The results show that the FD is able to differentiate between nonpathological and pathological subjects, while the other implemented method, i. e., CD, shows no significant difference. In summary, this paper shows that fractal descriptors are an appropriate support for analyzing the HRV, and therefore help to prevent or detect cardiovascular diseases. Especially Higuchi’s Fractal Dimension, well established in the analysis of time series, should get more attention in analyzing biomedical signals, such as HRV. Introduction An epidemiological update for the Cardiovascular Diseases (CVD) Statistics in Europe 2015 reports that cardiovascular diseases are the most common cause of mortality in Europe and globally. Furthermore, CVD causes almost two times as many deaths as cancer across Europe [1]. Several papers showed the significant relationship between Autonomic Nervous System (ANS) and cardiovascular mortality [2]. Heart Rate Variability (HRV) analysis is a useful noninvasive tool for understanding and characterizing the status of the ANS. Furthermore, HRV is an early predictor of cardiac failures [3]. The term HRV refers to the variation of beat-to-beat intervals. Usually, HRV is studied in an Electrocardiogram (ECG) by considering the time duration between two R-peaks. Therefore, the beat-to-beat intervals are often named RR-intervals. A schematic representation of an ECG-signal can be seen in Figure 1. The complex origin of biomedical signal limits the traditional linear approaches [4]. Hence, nonlinear methods gained recent interest to reveal more information embedded in the Heart Rate [5]. In this article two nonlinear indices are presented and compared.

Nonlinear Methods in Heart Rate Variability: Can they Distinguish between Nonpathological and Pathological Subjects?