Abstract
Lagged Poincaré plots have been successful in characterizing abnormal cardiac function. However, the current research practices do not favour any specific lag of Poincaré plots, thus complicating the comparison of results of different researchers in their analysis of heart rate of healthy subjects and patients. We researched the informative nature of lagged Poincaré plots in different states of the autonomic nervous system. It was tested in three models: different age groups, groups with different balance of autonomous regulation, and in hypertensive patients. Correlation analysis shows that for lag l = 6, SD1/SD2 has weak (r = 0.33) correlation with linear parameters of heart rate variability (HRV). For l more than 6 it displays even less correlation with linear parameters, but the changes in SD1/SD2 become statistically insignificant. Secondly, surrogate data tests show that the real SD1/SD2 is statistically different from its surrogate value and the conclusion could be made that the heart rhythm has nonlinear properties. Thirdly, the three models showed that for different functional states of the autonomic nervous system (ANS), SD1/SD2 ratio varied only for lags l = 5 and 6. All of this allow to us to give cautious recommendation to use SD1/SD2 with lags 5 and 6 as a nonlinear characteristic of HRV. The received data could be used as the basis for continuing the research in standardisation of nonlinear analytic methods.
Highlights
Heart rate variability (HRV) describes the variations between consecutive heartbeats, known as RR intervals
When the plot is adjusted by the ellipse-fitting technique, the analysis provides three indices: The standard deviation of instantaneous beat-to-beat interval variability (SD1), the continuous long-term RR interval variability (SD2), and the
Using K-means clustering on HF and low frequency (LF), we divided all healthy subjects into two groups with different balance of autonomic regulation [51]
Summary
Heart rate variability (HRV) describes the variations between consecutive heartbeats, known as RR intervals. The standard methods for HRV analysis include statistical (time domain), power spectral (frequency domain), and nonlinear geometrical analysis. Both linear and nonlinear methods are used to analyse heart rate in healthy subjects and patients with different pathologies [2,3,4]. The nonlinear methods usually supplement the linear ones [5,6,7,8,9,10,11,12]. There are publications examining gender differences in the nonlinear structure of HRV [20,21,22] and its variations throughout the times of day and night [22]. The additional clinical validation of existing novel methods is needed to Entropy 2017, 19, 523; doi:10.3390/e19100523 www.mdpi.com/journal/entropy
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