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

Abstract

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.

Challenging Recently Published Parameter Sets for Entropy Measures in Risk Prediction for End-Stage Renal Disease Patients

“…Furthermore, this complex optimization tends to exhibit chaotic properties [34,35]. Because entropy is a measure of unpredictability and thus quantifies these chaotic properties, it can be concluded that a reduced HRV entropy reflects an impairment of the heart rate regulation.…”

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19

“…Feature extraction and selection: In this stage, domain knowledge and data-driven approaches are utilized. In the domain knowledge, we selected the HRV measures among other vital signs mentioned in Table 1 due to many reasons: (a) the wellestablished connections in the literature between HRV features and pathological changes including inflammatory onsets [40,41], (b) the timely manner response of some HRV features such as the standard deviation of NN intervals (SDNN) and the root mean square of successive differences between normal heartbeats (RMSSD) [42]. Nonetheless, HRV is still nonspecific to certain inflammatory infections like the COVID-19.…”