Key result
Temporal variation analysis and entropy successfully quantify scatter point patterns for HRV classification.
The proposed temporal variation measure analysis provides a quantitative indicator (temporal variation entropy) to better extract physiological information from heart rate variability.
Answers are grounded in this paper and cite their sources.
Temporal variation entropy may refine HRV pattern analysis; leaves open clinical utility pending prospective validation.
In this study, an improved second‐order difference plot is proposed to analyze the variability of heart rate variability. Although the variation of the physiological status of the cardiovascular system can be shown graphically by the second‐order difference plot, the descriptive ability of existing indicators for this plot is insufficient. As a result, the physiological information contained in the second‐order difference plot cannot be extracted adequately. Addressing the problem, the temporal variation measure analysis is presented to describe distribution patterns of scatter points in the second‐order difference plot quantitatively and extract the acceleration information for a variation of heart rate variability. Experiment results demonstrate the effectiveness of the temporal variation measure analysis. As a quantitative indicator, the temporal variation entropy is properly designed and successfully applied in the recognition and classification of the physiological statuses of the heart.
No takes yet. Share an insight, caveat, or question.
Diao et al. (2022) studied Heart rate variability. Temporal variation measure analysis vs. Existing indicators was evaluated on Recognition and classification of the physiological statuses of the heart. Temporal variation measure analysis and temporal variation entropy successfully quantified scatter point distribution patterns in second-order difference plots for heart rate variability classification.
Synapse has enriched 10 related papers. Consider them for comparative context: