Time-Domain Measurements of Heart Rate Variability

Normative heart rate variability (HRV) values only apply when clients breathe at average rates (Shaffer & Ginsberg, 2017).

HRV time-domain indices quantify the amount of variability in measurements of the interbeat interval (IBI), which is the period between successive heartbeats. An IBI is called an R-R interval because it is the time between adjacent R-spikes. Graphic © arka38/Shutterstock.com



We measure the time intervals between successive heartbeats in milliseconds (ms). The software starts counting after detecting the first beat and calculates the first IBI in ms after detecting the second beat. This process is repeated until the end of the epoch or data collection period. Graphic courtesy of Dr. Richard Gevirtz.




In contrast, HRV frequency-domain measurements calculate the absolute or relative amount of signal power in the ULF, VLF, LF, and HF bands. The graphic below shows two methods of measuring the spectral distribution of HRV power (FFT and Autoregression). The graphic is courtesy of Tarvainen and Niskanen (2020).

BCIA Blueprint Coverage

This unit addresses IV. HRV Measurements: A. Time-domain measurements and their meaning, properties, and correlates.
 
Professionals completing this unit will be able to discuss the following HRV time-domain indices:
A. SDNN
B. SDRR
C. SDANN
D. pNN50
E. NN50
F. HR Max - HR Min
G. RMSSD
H. HRV triangular index



This unit covers the SDNN, SDRR, SDANN, pNN50, NN50, HR Max - HR Min, RMSSD, and HRV triangular index.

Please click on the podcast icon below to hear a full-length lecture.

SDNN

The SDNN is the standard deviation of the interbeat interval of normal sinus beats measured in milliseconds (ms). "Normal" means that abnormal beats, like ectopic beats, have been removed. The related SDSD, the standard deviation of successive RR interval differences, only represents short-term variability.

SDNN is calculated using data that are free of artifacts and abnormal heartbeats.

SNS and PNS activity contribute to SDNN, which is highly correlated with ULF, VLF, LF band power, and total power (Umetani et al., 1998). This relationship depends on the measurement conditions. When these bands have greater power than the HF band, they contribute more to SDNN.

In short-term (≤ 5 minutes) resting recordings, the primary source of the variation is parasympathetically-mediated RSA, especially with slow-paced breathing protocols (Shaffer, McCraty, & Zerr, 2014).

In 24-hour recordings, LF band power contributes significantly to SDNN (Kusela, 2013).

Why 24-Hour Recording is More Accurate

The SDNN is more accurate when calculated over 24 hours than during the shorter periods monitored during biofeedback sessions. More extended recording periods provide data about cardiac reactions to a greater range of environmental stimulation. In addition to cardiorespiratory regulation, extended measurement periods can index the heart's response to changing workloads, anticipatory central nervous activity involving classical conditioning, and circadian processes, including sleep-wake cycles (Lehrer, 2012). Twenty-four-hour recordings reveal the SNS contribution to HRV (Grant et al., 2011).

While the conventional brief recording standard is 5 minutes, researchers have proposed ultra-short-term (UST) recording periods from 60 (Salahuddin et al., 2007; Shaffer, Meehan, & Zerr, 2020) to 240 seconds (Baek et al., 2015).

24-Hour SDNN Predicts Mortality

The SDNN is the "gold standard" for medical stratification of cardiac risk when recorded 24 hours (Task Force, 1996). SDNN values predict both morbidity and mortality. Based on 24-hour monitoring, patients with SDNN values below 50 milliseconds are classified as unhealthy, 50-100 milliseconds have compromised health, and above 100 milliseconds are healthy.




Heart attack survivors, whose 24-hour measurements placed them in a higher category, had a greater probability of living during a 31-month mean follow-up period. For example, patients with SDNN values over 100 milliseconds had 5.3 times lower mortality risk at follow-up than those under 50 milliseconds (Kleiger et al., 1987). Does this mean training patients to increase SDNN to a higher category could reduce their mortality risk?

A Firstbeat Bodyguard 2, which is designed for ambulatory 24-hour monitoring, is shown below.

SDRR

The SDRR is the standard deviation of the interbeat interval for all sinus beats (including abnormal or false beats) measured in ms. As with the SDNN, the SDRR calculates how these intervals vary over time. The SDRR is also more accurate when calculated over 24 hours. Abnormal beats may reflect cardiac dysfunction or noise that masquerades as HRV. Below is a BioGraph ® Infiniti heart rate variability display. The roller coaster accelerates as SDRR increases.

SDANN

The SDANN is the standard deviation of the average NN intervals for each of the 5-minute segments during a 24-hour recording. NN intervals stands for normal-to-normal intervals. These are "clean" IBIs calculated after artifacting the data. The SDANN estimates heart rate (HR) changes produced by cycles longer than 5 minutes. Like the SDNN, it is measured and reported in milliseconds. This index correlates with the SDNN and is generally considered redundant (Shaffer, McCraty, & Zerr, 2014). Minimum HR is more strongly associated with Ln SDANN than Ln RMSSD. Ln means the natural logarithm. Maximum heart rate is weakly and inconsistently correlated with these time-domain measures (Burr et al., 2006).

SDNN INDEX (SDNNI)

The SDNN Index (SDNNI) is the mean of the standard deviations of all the NN intervals for each 5-minute segment of a 24-hour HRV recording. Therefore, this measurement only estimates variability due to the factors affecting HRV within 5 minutes. It is calculated by dividing the 24-hour record into 288 5-minute segments and then calculating the standard deviation of all NN intervals within each segment. The SDNNI is the average of these 288 values.

The SDNNI is believed to measure autonomic influence on HRV primarily. The SDNNI correlates with VLF power over 24 hours (Shaffer, McCraty, & Zerr, 2014).

NN50

The NN50 measures the number of adjacent NN intervals that differ by more than 50 milliseconds. At least a 2-minute sample is required.

pNN50

The pNN50 is the percentage of adjacent NN intervals that differ by more than 50 milliseconds. While the conventional minimum recording is 5 minutes, researchers have proposed UST periods of 10 seconds (Salahuddin et al., 2007), 30 seconds (Baek et al., 2015), and 60 seconds (Shaffer, Meehan, & Zerr, 2020).

The pNN50 is closely correlated with PNS activity (Umetani et al., 1998). It correlates with the RMSSD and HF power. However, the RMSSD typically provides a better assessment of RSA (especially in older subjects), and most researchers prefer it to the pNN50 (Otzenberger et al., 1998). The pNN50 may be a more reliable index than short-term SDNN measurements for the brief samples used in biofeedback.

HR Max-HR Min

HR Max – HR Min is the average difference between the highest and lowest heart rates during each respiratory cycle. This is the easiest time-domain metric for clients to understand. Where the RMSSD and SDNN are abstract, clients can easily visualize a wave's height (Moss, 2022).

At least a 2-minute sample is required to calculate HR Max – HR Min. Physically active individuals show wider peak-trough differences than those who are sedentary.

HR Max-HR Min is Affected by Breathing Rate and Measures RSA

This index is susceptible to the effects of respiration rate, independent of vagus nerve traffic. Instead of directly indexing vagal tone, it reflects RSA. HR Max-HR Min depends on age and fitness. Since longer exhalations allow greater acetylcholine metabolism, slower respiration rates can produce higher RSA amplitudes that are not mediated by changes in vagal firing (Lehrer, 2012).




HR Max-HR Min can reach 50 beats per minute for elite athletes. This measure is used for HRV assessment in paced breathing protocols and is highly correlated with the SDNN and RMSSD (Shaffer, McCraty, & Zerr, 2014).

RMSSD

The RMSSD is the root mean square of successive differences between normal heartbeats. This value is obtained by first calculating each subsequent time difference between adjacent interbeat intervals in milliseconds. Then, each value is squared, and the result is averaged before the square root of the total is obtained.

The RMSSD reflects rapid beat-to-beat variance in HR and better estimates vagal activity than SDNN (Shaffer, McCraty, & Zerr, 2014). The RMSSD is conceptualized as vagally-mediated HRV (vmHRV; Jarczock et al., 2021). The RMSSD is the best overall measure of short-term HRV because it is less affected by outliers and artifacts than SDNN (Gevirtz, 2020).

HRV Triangular Index

The HRV triangular index (HTI) is a geometric measure based on 24-hour recordings, which calculates the integral of the RR interval histogram's density divided by its height (Task Force, 1996). Graphic retrieved from vippng.com.




The PNS and SNS contribute to the HRV triangular index (Billman et al., 1982; Schwartz et al., 1988). A 5-minute epoch may be sufficient to represent this metric (Jovic & Bogunovic, 2011). A 120-second UST period estimated this metric (Shaffer, Meehan, & Zerr, 2020).

Summary Tables

Table 2 shows the minimum conventional and UST recording periods (Shaffer, Meehan, and Zerr, 2020).




Table 3 displays Kubios time-domain calculations after artifact correction.

The Triangular Interpolation of the NN Interval Histogram

The Triangular Interpolation of the NN Interval Histogram (TINN) is the baseline width of a histogram displaying NN intervals. To unpack this definition, visualize a histogram that plots the frequency of NN intervals. The X-axis represents interbeat interval length in milliseconds, and the Y-axis represents the number of intervals of identical length (Yilmaz et al., 2018). At least a 5-minute sample is required (Shaffer & Ginsberg, 2017). Graphic retrieved from vippng.com.

HRV Myths

Misconception: Short-term and 24-hour measurements are interchangeable.

You cannot interpret short-term metrics using 24-hour norms because they were obtained under different conditions. Briefer recording periods generally underestimate HRV.

Misconception: We can interpret 5-minute slow-paced breathing measurements using 5-minute resting norms.

A resting condition means that participants breathe at typical rates (e.g., 12-14 bpm). You cannot compare its values with resting norms since slow-paced breathing is less than half that rate and increases RSA.

Misconception: We can use UST and short-term measurements interchangeably.

UST measurements are more vulnerable to corruption by artifact because they are based on fewer data points. Currently, there is no consensus on acceptable UST-measurement length. "UST measurements are proxies of proxies. They seek to replace short-term values, which, in turn, attempt to estimate reference standard long-term metrics" (Shaffer, Meehan, & Zerr, 2020). 





A clinician calculates an SDNN value of 60 milliseconds from a 15-minute resting baseline and is concerned that their client may have an elevated heart attack risk. What have they overlooked?

They mistakenly applied cutoffs based on 24-hour recordings to brief recordings. Twenty-four-hour and brief recording values are not interchangeable since short monitoring periods exclude long-term sources of HRV like circadian rhythms.

Glossary

heart rate: the number of heartbeats per minute, also called stroke rate.

heart rate variability (HRV): the beat-to-beat changes in HR involving changes in the RR intervals between consecutive heartbeats.

HR Max – HR Min: an HRV index that calculates the average difference between the highest and lowest HRs during each respiratory cycle.

HRV triangular index (HTI): a geometric measure based on 24-hour recordings, which calculates the integral of the RR interval histogram's density divided by its height.

interbeat interval (IBI): the time interval between the peaks of successive R-spikes (initial upward deflections in the QRS complex).

NN50: the number of adjacent NN intervals that differ from each other by more than 50 milliseconds.

NN interval: the normal-to-normal interval is an IBI after removing artifacts.

pNN50: the percentage of adjacent NN intervals that differ by more than 50 milliseconds.

RMSSD: the square root of the mean squared difference of adjacent NN intervals.

SDANN: the standard deviation of the average NN intervals (mean heart rate) for each of the 5-minute segments during a 24-hour recording.

SDNN: the standard deviation of the normal (NN) sinus-initiated IBI measured in milliseconds.

SDNN index (SDNNI): the mean of the standard deviations of all the NN intervals for each 5-minute segment of a 24-hour HRV recording.

SDRR: the standard deviation of the interbeat interval for all sinus beats measured in milliseconds, which predictsmorbidity and mortality.

Triangular Interpolation of the NN Interval Histogram (TINN): the baseline width of a histogram displaying NN intervals.

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Visit the BioSource Software Website

BCIA offers two HRV Biofeedback Certification paths: Biofeedback and Neurofeedback. For Biofeedback, BioSource Software offers Human Physiology to satisfy BCIA's Human Anatomy & Physiology requirement. For Neurofeedback, BioSource provides Physiological Psychology to satisfy BCIA's Physiological Psychology requirement.

BCIA has accredited each course, and they combine affordable pricing ($150) with industry-leading content.

Assignment

Now that you have completed this module, review the missed beat and extra beat graphics and see whether you can identify abnormally long and short IBIs. Based on this module, how might you improve your artifacting.

Assignment

Now that you have completed this module, identify the index that is the "gold standard" for predicting the risk of morbidity and mortality when based on 24-hour recording. Which index should be most easily understood by your clients? Why?

References

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Booiman, A. (2017). Personal communication regarding HR Max-HR Min slow-paced breathing in the Netherlands.

Burr, R. L., Motzer, S. A., Chen, W., Cowan, M. J., Shulman, R. J., & Heitkemper, M. M. (2006). Heart rate variability and 24-hour minimum heart rate. Biol Res Nurs, 7(4), 256-267. https://doi.org/10.1177/1099800405285268

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