Why the study?
Do time domain analysis, frequency domain analysis, Poincaré graphs, and fractal analysis accurately estimate heart rate variability compared to direct ECG measurement?
Do time domain analysis, frequency domain analysis, Poincaré graphs, and fractal analysis accurately estimate heart rate variability compared to direct ECG measurement?
Standard HRV analysis methods may underestimate heart rate variability in individuals with higher baseline variability compared to direct ECG measurement.
Alternative HRV methods may underestimate values in high-variability subjects; leaves open equivalence to direct ECG.
Oscillation around a mean value is found in the intervals between consecutive heartbeats (R-R intervals). This oscillation is known as heart rate variability (HRV). Various methods can be used to estimate HRV, but the extent to which agreement exists between the results obtained by the different methods is not known. In this study HRV, as determined directly from the ECG, is compared to results obtained by time domain analysis, frequency domain analysis, Poincaré graphs and fractal analysis. Results showed that, in individuals with low HRV values, the results obtained by time domain analysis, frequency domain analysis, Poincaré graphs and alpha 1 of fractal analysis are comparable to those calculated directly from the ECG. However, time domain analysis, frequency domain analysis, Poincaré graphs and fractal analysis are less sensitive than direct ECG measurements in individuals with higher HRV.
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Grant et al. (2006) studied this question.
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