Heart rate variability (HRV) quantifies beat-to-beat fluctuations in heart rate and is considered an index of cardiac parasympathetic nervous system activity [1–3]. 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]. For our study we focused on two time-domain HRV measurements: the Standard Deviation of the normal-to-normal intervals (SDNN) and the Root Mean Square of Successive Differences (RMSSD) between adjacent NNs. Both are easy to calculate and among the most widely used indices of HRV [7]. SDNN estimates overall HRV, while RMSSD estimates short-term components of HRV [4]. However in both clinical practice and research, ECGs of 10s or 20s are routinely collected and constitute a vast and potentially valuable resource. Additionally, 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].

Currently it is not known to what extent (ultra-)short ECG recordings of 10s to 20s manage to capture the “actual” HRV of a subject at rest. Only a limited number of studies have specifically investigated the validity [9,10] and reproducibility [8,11] of (ultra-)short HRV measurements. However, these studies had very small sample sizes (n≤70 [10]). In addition these studies used (intra-class) correlation coefficients between two measurements of different recording lengths, but this does not account for neither the potential differences in means between two measurements [12,13] nor communicates the degree of the differences [14].

Given these methodological limitations in the existing literature [9–11] we investigated in a sample of 3,387 subjects to what extent (ultra-)short recordings capture the “actual” HRV. We evaluated recordings of 10s, 30s, and 120s selected from the longest (gold-standard) recording of 240s to 300s. SDNN and RMSSD measured from the (ultra-)short recordings were compared to the gold standard [12,13]. In addition to correlation coefficients we calculated Bland-Altman’s 95% limits of agreement (LoA) and Cohen’s d statistics to evaluate the agreement of SDNN and RMSSD measured from (ultra-)short recordings and the gold standard.

In our sample of 3,387 subjects the mean age was 53 years and 51% were women. The average total recording length was 294s (min-max:239-302s) with a total population heart rate average of 68(SD:±10) beats per minute. At the second screening, our total population had 6.7% of subjects with a recent cardiovascular event, 39% were hypertensive, 7.6% had diabetes mellitus type 2, 34% had hypercholesterolemia and 22% had chronic kidney disease. Median values for SDNN and RMSSD were similar for males and females (see Table 1). We observed the usual strong inverse correlation with age for both SDNN (r = -0.30) and RMSSD (r = -0.28). The 3,387 subjects used in the current study that had good quality HRV measures available constituted about half of the total sample size of the second screening of PREVEND. As shown in Table 2 characteristics of the subjects used in the current study were very similar to those of subjects not used in the current study. Table 3 shows the raw and natural log transformed SDNN and RMSSD categorized by recording length. It shows that the mean values of RMSSD and, particularly, SDNN increased for longer recording lengths. This increase was 1.32ms for the mean RMSSD (from 28.16 for Avg10s to 29.48 for the total recording), while the mean SDNN increased 9.94ms (from 25.87 for Avg10s to 35.81 for the total recording).

In order to investigate the utility of routine 10s ECG recordings for HRV calculation in large-scale epidemiologic studies we evaluated the agreement of SDNN and RMSSD between (ultra-)short recordings and a gold-standard recording of 240s to 300s in 3,387 adults. We showed that RMSSD consistently outperformed SDNN. RMSSD measured from recordings of only 10s in length are already reliable and good proxies for those measured from longer recording lengths (240s-300s), in particular when the measurements from multiple 10s recordings are averaged. For SDNN the measurements from 10s recordings were reliable, but although they correlated moderately (for the single recordings) to strongly (for Avg10s) with the gold standard, agreement was poor in both cases (i.e. Cohen’s d close to 1) and hence are bad proxies. For SDNN measured from 30s recordings the agreement with the gold standard was still only moderate, but sufficient to yield reliable estimates of “actual” SDNN. SDNN and RMSSD measured from 120s recordings were both in high agreement with the gold-standard recordings.

Our findings that RMSSD measured from 10s recordings is a good proxy for the “actual” RMSSD, but that this doesn’t hold for SDNN, are in line with previous studies [8–11]. All of these also observed that measurements from ultra-short recordings yield good estimates of RMSSD, while for SDNN the agreement is not sufficient to provide reliable estimates for the “actual” SDNN. In addition we and others observed that the correlation or agreement increased with an increase of the recording length for RMSSD and especially for SDNN [10,11]. The high dependence of SDNN on recording length is to be expected because SDNN reflects the total power of all HRV frequency components combined whereas RMSSD is a reflection of high frequency HRV components only [3]. Furthermore in line with our findings others have shown that averaging HRV measures obtained from sequential time periods reduces the error imposed by the analysis of very short segments [8,11]. We found that the reliability of the three individual 10s recording periods was substantial, in particular for RMSSD.

In our study we chose to extract the (ultra-)short recordings from the total recording length to specifically address our research question whether HRV measured from (ultra-)short recordings reflect the “actual” HRV. Our design differs from that of Schroeder and colleagues [11], who measured HRV at sequential time periods. Their design is more suited to assess the repeatability (or reliability) of HRV measurements, while our study design reflects our focus on the validity of (ultra-)short recordings for HRV measurements in the time domain (SDNN, RMSSD) compared to a gold-standard recording period of 240s to 300s. A consequence of our study design is that the measurements of the (ultra-)short recordings are not independent of the total recording and hence correlations and agreement measures are expected to be inflated. Nevertheless for both HRV measures all observed correlations were significantly higher and all 95%LoAs significantly smaller than those simulated under the null hypothesis supporting the validity of HRV measurements based on (ultra-)short recordings. The biases and Cohen’s d for both HRV measures did not differ from the expectation. This can be explained by the fact that the distributions of the simulated HRV measures from the (ultra-)short and total recordings are similar to those of the observed ones, leading to similar mean differences between the HRV measurements of the gold standard and those of the different (ultra-)short recording periods. However, the variation in those paired differences between the observed measurements of the (ultra-)short segments and those of the total recording is smaller than from the respective paired differences of the simulated measurements, explaining the much higher correlation and tighter 95%LoAs.

In this study we analyzed a general population in which the mean age was 53 years and both sexes were included [15,16]. Previous studies [8–11] only included healthy individuals and Dekker et al. [8] further limited their study population to young men (mean ± SD age 25.9±3.8years), thereby reducing the generalizability of their results even more. Therefore our results are more representative of the general population. However, 10s ECGs in cases with cardiac arrhythmias should be used with caution because given the very low number of beats in 10s, one artefact caused by cardiac arrhythmia will make up about 5% of the total duration of the recording depending on the heart rate. Therefore for calculating RMSSD and SDNN we suggest the following criteria: (a) one artefact (of any kind such as detection failure or arrhythmia, harmless or not) at the beginning or at the end of a recording should be excluded and the remaining part of the segment should be used, and (b) other artefacts, not at the beginning or at the end, or more than one, means the exclusion of the entire segment. This is because we would need a continuous segment to calculate the successive differences (i.e. SDNN and RMSSD) and one interruption would imply a great loss of successive differences.

A major strength of our study was the large sample size of 3,387 subjects, which allowed for precise estimates of agreement measures between different recording periods. Furthermore the significance of our study is reinforced by our statistical approach. We calculated not only Pearson’s correlation coefficients to measure the strength of linear association between the recordings, but also used Bland-Altman’s statistics [12,13] and Cohen’s d [14] to evaluate the degree of bias. As pointed out by Altman and Bland correlation coefficients are not sufficient to demonstrate the agreement of measurements [12, 13]. No previous studies have used these different agreement analysis techniques. The importance of considering measurements of differences is demonstrated when comparing our results of the Pearson’s correlation coefficients and Cohen’s d statistics. For instance a substantial decrease in Cohen’s d statistic from Avg10s to 30s is shown for SDNN, while the Pearson’s correlation coefficients remains the same. Therefore, only considering Pearson’s correlation coefficient results for SDNN would lead to an erroneous interpretation.

Unlike other studies [8–11] that also measured frequency domain HRV parameters such as the high frequency (HF) component we limited our study to time domain parameters RMSSD and SDNN. This was because ECGs of less than 60s duration are not sufficient to assess the HF components and ECGs of at least 120s should be used to address the low frequency components [1–3]. Therefore our conclusions do not apply to HRV parameters in the frequency domain.

An important implication of our study is that 10s ECG recordings could be used for calculating time-domain HRV parameters, particularly RMSSD, in future epidemiologic studies. In standard in-clinic evaluation of heart rate dynamics, 300s is the recommended length of measurement [3]. Nevertheless, 10s recordings from 12-lead ECGs are already commonly used to detect resting abnormalities in interval lengths, wave morphology and segment elevation/depressions [10] and have already shown their usefulness as diagnostic tool [5,6,21]. For example, reduced HRV measured from three 10s ECG recordings was recently found to be associated with an increased incidence of heart failure [21]. An example of our findings applicability is genome-wide association studies (GWAS), where large sample sizes are needed to detect small effects of genetic variants. A large number of cohorts may have short ECG recordings available but may not (yet) have measured RMSSD (and SDNN). The increase in sample size when using RMSSD (and SDNN) from these cohorts in a GWAS will most likely outweigh the loss in accuracy of the phenotype measurements and hence permit the identification of more genetic variants.

In summary, from our unprecedented large sample size, the selection of our (ultra-)short recording from our total recording, our careful data processing and our sophisticated statistical analysis we can conclude that particularly RMSSD from (ultra-)short recordings manages to capture HRV well. Even a single 10s (standard) ECG recording yields a valid RMSSD measurement, although averaging over multiple 10s ECGs is preferable. For SDNN we would recommend recordings of at least 30s or, if not available, multiple 10s ECGs. In addition, our study suggests that it is unnecessary to use recordings longer than 120s to obtain accurate measures of RMSSD and SDNN.