Abstract
ObjectivesIn order to investigate the applicability of routine 10s electrocardiogram (ECG) recordings for time-domain heart rate variability (HRV) calculation we explored to what extent these (ultra-)short recordings capture the “actual” HRV.MethodsThe standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD) were measured in 3,387 adults. SDNN and RMSSD were assessed from (ultra)short recordings of 10s(3x), 30s, and 120s and compared to 240s–300s (gold standard) measurements. Pearson’s correlation coefficients (r), Bland-Altman 95% limits of agreement and Cohen’s d statistics were used as agreement analysis techniques.ResultsAgreement between the separate 10s recordings and the 240s-300s recording was already substantial (r = 0.758–0.764/Bias = 0.398–0.416/d = 0.855–0.894 for SDNN; r = 0.853–0.862/Bias = 0.079–0.096/d = 0.150–0.171 for RMSSD), and improved further when three 10s periods were averaged (r = 0.863/Bias = 0.406/d = 0.874 for SDNN; r = 0.941/Bias = 0.088/d = 0.167 for RMSSD). Agreement increased with recording length and reached near perfect agreement at 120s (r = 0.956/Bias = 0.064/d = 0.137 for SDNN; r = 0.986/Bias = 0.014/d = 0.027 for RMSSD). For all recording lengths and agreement measures, RMSSD outperformed SDNN.ConclusionsOur results confirm that it is unnecessary to use recordings longer than 120s to obtain accurate measures of RMSSD and SDNN in the time domain. Even a single 10s (standard ECG) recording yields a valid RMSSD measurement, although an average over multiple 10s ECGs is preferable. For SDNN we would recommend either 30s or multiple 10s ECGs. Future research projects using time-domain HRV parameters, e.g. genetic epidemiological studies, could calculate HRV from (ultra-)short ECGs enabling such projects to be performed at a large scale.
Highlights
Heart rate variability (HRV) quantifies beat-to-beat fluctuations in heart rate and is considered an index of cardiac parasympathetic nervous system activity [1,2,3]
Agreement increased with recording length and reached near perfect agreement at 120s (r = 0.956/Bias = 0.064/d = 0.137 for Standard Deviation of the normal-tonormal intervals (SDNN); r = 0.986/Bias = 0.014/d = 0.027 for Root Mean Square of Successive Differences (RMSSD))
Future research projects using time-domain HRV parameters, e.g. genetic epidemiological studies, could calculate HRV fromshort ECGs enabling such projects to be performed at a large scale
Summary
Heart rate variability (HRV) quantifies beat-to-beat fluctuations in heart rate and is considered an index of cardiac parasympathetic nervous system activity [1,2,3]. In the general population reduced HRV has been associated with increased risk of coronary heart disease [4], cardiac mortality [5], and all-cause mortality [6]. HRV is calculated from time series of beat-to-beat heart-rate data [3]. SDNN estimates overall HRV, while RMSSD estimates short-term components of HRV [4]. In both clinical practice and research, ECGs of 10s or 20s are routinely collected and constitute a vast and potentially valuable resource. Short-term recordings are suitable for large scale studies, because they impose a minimal burden on the subject and can be made under standardized conditions [8]
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