Key result
Three-dimensional Poincaré plot analysis and local distribution entropy effectively quantified the temporal and spatial patterns of RR interval time series using PhysioNet/PhysioBank data.
Why the study?
Standard Poincaré plot analysis inherently ignores the embedding temporal information of RR interval time series, preventing adequate reflection of temporal variation in heart rate variability.
A novel three-dimensional Poincaré plot analysis with local distribution entropy improves the quantification of temporal and spatial patterns in heart rate variability compared to standard methods.
May refine RR interval pattern analysis in research; leaves open clinical utility pending validation.
For the limitation of Poincaré plot analysis, the three‐dimensional Poincaré plot analysis is proposed to analyze the heart rate variability. Firstly, the Poincaré plot and some classic indicators are briefly discussed. Because the standard analysis method inherently ignores the embedding temporal information of the RR interval time series, the temporal variation of the time series cannot be adequately reflected. Secondly, for the limitation of the Poincaré plot analysis, a three‐dimensional Poincaré plot is presented, which can fully describe the temporal and spatial characteristics of the RR interval time series. Thirdly, we propose the local distribution entropy, which can quantify the temporal and spatial patterns of the scatter points in novel space. Finally, PhysioNet/PhysioBank is applied in this study. The experimental results demonstrate the effectiveness of the three‐dimensional Poincaré plot analysis.
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Wang et al. (2022) studied Heart rate variability. Three-dimensional Poincaré plot analysis vs. Standard Poincaré plot analysis was evaluated on Temporal and spatial characteristics of the RR interval time series. Three-dimensional Poincaré plot analysis and local distribution entropy effectively quantified the temporal and spatial patterns of RR interval time series using PhysioNet/PhysioBank data.
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