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Correlation between time-domain measures of heart rate variability and scatterplots in postinfarction patients
X Copie1, J Y Le Heuzey, M C Iliou
1Department of Cardiology, Broussais Hospital, Paris, France.
Insights
Scatterplots offer a simple method to assess heart rate variability (HRV) by correlating with established time-domain measures. This technique provides insights into both long-term and short-term HRV, aiding in cardiac autonomic function evaluation.
Area of Science:
- Cardiology
- Biomedical Engineering
- Physiology
Background:
- Heart rate variability (HRV) is crucial for assessing cardiac autonomic function.
- Traditional time-domain and frequency-domain analyses have limitations in capturing beat-to-beat variability.
- The relationship between scatterplots and conventional HRV measures remains unclear.
Purpose of the Study:
- To investigate the correlation between scatterplot parameters (length, width, area) and established time-domain HRV measures.
- To determine if scatterplots can provide information on both long-term and short-term HRV.
- To explore the potential of scatterplots for assessing cardiac parasympathetic modulation.
Main Methods:
- Analysis of RR interval data from 50 postinfarction patients.
- Generation of scatterplots by plotting each RR interval against the preceding one.
- Measurement of scatterplot length and width, with area calculated as an ellipse.
- Correlation analysis between scatterplot dimensions and time-domain HRV indices (SDNN, SDANN, pNN50, variability index).
Main Results:
- Strong correlations were found between scatterplot length and long-term HRV indices (SDNN, SDANN) (r > 0.9, P < 0.0001).
- Significant correlations were observed between scatterplot width and short-term HRV parameters (pNN50, variability index) (r > 0.9, P < 0.0001).
- Scatterplot width measurement at varying RR intervals shows potential for evaluating short-term HRV across different heart rates.
Conclusions:
- Scatterplots provide a straightforward visual representation of HRV, correlating well with established time-domain measures.
- Scatterplot dimensions effectively reflect both long-term and short-term HRV.
- Scatterplot analysis offers a promising, simple method for assessing cardiac parasympathetic activity at different heart rates.
Abstract:
Heart rate variability (HRV) is usually measured in time or frequency domains. Beat-to-beat variability, which cannot be assessed by frequency-domain analysis, and can only be assessed globally by time-domain analysis, provides information concerning the nonlinear behavior of heart rate. This beat-to-beat variability can be displayed on scatterplots, where each RR interval is plotted against the preceding RR interval. However, the relationship between scatterplots and other measures of HRV is unknown. We studied the correlations between time-domain measures and scatterplot length, width, and area in 50 postinfarction patients. Scatterplot length and width were measured after printing. Scatterplot area was calculated from length and width, assimilating the plot to an ellipse. Long-term variability indexes (SDNN and SDANN) were strongly correlated with scatterplot length (r > 0.9, P < 0.0001), and short-term variability parameters (pNN50 and variability index) with scatterplot width (r > 0.9; P < 0.0001). Scatterplots are, therefore, a simple way of providing information concerning long- and short-term HRV. Furthermore, measurement of scatterplot width at different given RR intervals could be an approach to the evaluation of short-term HRV for different heart rates. This could provide a simple way of assessing cardiac parasympathetic modulation at different heart rates.