Abstract
The time series of interbeat intervals of the heart reveals much information about disease and disease progression. An area of intense research has been associated with cardiac autonomic neuropathy (CAN). In this work we have investigated the value of additional information derived from the magnitude, sign and acceleration of the RR intervals. When quantified using an entropy measure, these time series show statistically significant differences between disease classes of Normal, Early CAN and Definite CAN. In addition, pathophysiological characteristics of heartbeat dynamics provide information not only on the change in the system using the first difference but also the magnitude and direction of the change measured by the second difference (acceleration) with respect to sequence length. These additional measures provide disease categories to be discriminated and could prove useful for non-invasive diagnosis and understanding changes in heart rhythm associated with CAN.
Highlights
Biological signals, including electrocardiograms (ECG) or the electrical activity of the heart, exhibit complex dynamics which are characterized by nonlinearity and nonstationarity and often include random noise due to movement artefacts [1]
The major advantage of Rényi entropy is that it is robust for short time series, nonlinearity and nonstationarity
The Rényi entropy introduced here has the advantage of addressing how the probabilities are calculated by applying a density method rather than a histogram method, which is the standard for calculation of multiscale entropy [49]
Summary
Biological signals, including electrocardiograms (ECG) or the electrical activity of the heart, exhibit complex dynamics which are characterized by nonlinearity and nonstationarity and often include random noise due to movement artefacts [1]. Physiological dynamics of the heartbeat time series change with healthy aging [12,13] and disease, and during different activities such as sleeping [14,15] and exercise [16,17,18,19,20,21]. For all physiological and pathophysiological models of autonomic function, heart rate variability (HRV) is calculated from the cardiac interbeat intervals (IBI) of the time series. All models assume that the extrinsic modulation of the heartbeat by the autonomic nervous system (ANS) and the endocrine system affect HRV by either increasing the interbeat interval (parasympathetic influence), or decreasing the interbeat interval (sympathetic influence), or a combination of both. Disturbance of the ANS modulation by Entropy 2019, 21, 727; doi:10.3390/e21080727 www.mdpi.com/journal/entropy
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