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What is the difference between RR-intervals and NN-intervals in HRV-data?

What is the difference between RR-intervals and NN-intervals in HRV-data?


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Heart rate variability (HRV) is often used as a measure of the sympathetic nervous system. One way to quantify HRV is by calculating the Inter-beat interval (IBI), also referred to as the RR-interval. The RR-interval refers to the time between two R-peak of a traditional ECG heart-beat waveform (see Figure 1).


Figure 1: Two subsequent heart-beats with component names. The RR interval is the time between two subsequent R-peaks. Picture taken from: http://lifeinthefastlane.com/ecg-st-segment-evaluation/

However, in a review article by Shaffer, McCraty and Zerr (2014) they refer to normal-to-normal (NN) intervals instead. This extends to the statistical measures of the RR intervals, such as SDRR, pRR50 (Urooj, Gupta, Sp & Tandon, 2014), i.e., SDNN and pNN50, respectively (Shafer et al., 2014). What is the difference between RR-intervals and the NN-intervals?


References

Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Frontiers in psychology, 5, 1040.

Urooj, M., Gupta, S., Sp, V., & Tandon, M. (2014). REFERENCE RANGE OF HEART RATE VARIABILITY AND VALIDATION IN SUBJECTS WITH ASYMPTOMATIC ELEVATED LIVER FUNCTION ENZYMES. International Journal of Current Pharmaceutical Research, 6(4), 49-52.


The difference is rather simple. NN-intervals refer to the intervals between normal R-peaks. During a measurement, artifacts may arise due to arrhythmic events or faulty sensors, for example (Citi, Brown & Barbieri, 2012). This may lead to abnormal R-peaks, which may in turn distort the statistical measures. To ensure reliable and valid data, only normal R-peaks are selected. Alternatively, the abnormal R-peaks can be corrected.

In practice, however, RR-intervals and NN-intervals are synonymous (Tarvainen, 2014; Wiki). The use of "NN-intervals" is merely used to emphasize that normal R-peaks were used.


References

Citi, L., Brown, E. N., & Barbieri, R. (2012). A real-time automated point-process method for the detection and correction of erroneous and ectopic heartbeats. IEEE transactions on biomedical engineering, 59(10), 2828-2837.

Tarvainen, M.P. (2014). Kubios HRV version 2.2: User's Guide.

https://en.wikipedia.org/wiki/Heart_rate_variability


What Do HRV Values Mean?

Measuring HRV on a machine and understanding what these HRV measurements mean are two entirely different tasks. With that in mind, let’s break down the intricacies of this measurement in more detail.

“Heart rate variability is based on a set of numbers generated from the times between heartbeats” says Stein, or the periods of time that occur between two beats. These are also known as RR intervals and are measured in milliseconds. The RR interval refers to the time between two peaks on a traditional ECG heartbeat waveform.

Additionally, NN intervals refer to the intervals between two normal R-peaks. In practice, RR intervals and NN intervals are the same, and the use of NN intervals merely emphasizes that normal R peaks, like the one pictured below, were used.

Another term worth remembering during HRV analysis is the root mean square of standard deviation, or RMSSD for short. The root mean square of standard deviation is simply the successive differences between neighboring RR intervals. According to researchers Fred Shaffer and J. P. Ginsberg, we can calculate this “by first calculating each successive time difference between heartbeats in milliseconds. Then, each of the values is squared and the result is averaged before the square root of the total is obtained.”

Methods by which we can analyze HRV data and extract this information exist in either the time-domain or frequency-domain. Each method of analysis is different, but both contain a plethora of information.

Time-domain analysis, for example, is often used in clinical applications of HRV and is the simplest method of HRV analysis. Time-domain analysis utilizes interbeat intervals to measure NN intervals and RMSSD.

Frequency-domain techniques, on the other hand, “are performed on the interbeat interval signal, a plot of the RR intervals versus time or beat number. It is very important with frequency-domain techniques that the data points be equidistant,” according to Harvard Bioscience.


Abstract

The quality of life and individual well-being are crucial factors in disease prevention. Particularly, healthy lifestyle lessens the risk and occurrence of main diseases, such as cardiovascular diseases and metabolic disorders. Since a patient has an active role in being a co-producer of his/her health, innovative devices and technologies have been devoted to helping folks in self-evaluation and expected to play a key role to maintain their well-being. In this work, we present a very promising assessment tool for health, Heart Rate Variability (HRV). HRV is the difference in time between one heartbeat and the next. HRV measurement is simple and non-invasive, it is derived from recording of electrocardiogram (ECG) on free-moving subjects. The main aim of this work is to investigate the dynamics in the autonomic regulation of the heart rate by using frequency and temporal analysis to correlate between the HRV and these physiological patterns.

In addition to the applied frequency and temporal analyses, pattern recognition is also accomplished using Neural Networks which are further implemented and explored in this work. In the first place, the detection of the sleep/awake states is achieved. Next, a multiclassification of different types of activities such as sleeping, walking, exercising and eating is performed.


Background

Heart rate variability (HRV) is a noninvasive method used to quantify fluctuations in the time interval between normal heart beats. The purpose of this study was to compare the autonomic nervous system functioning of patients with burns to healthy participants after their burn scars had been re-epithelialized.

Materials and methods

The authors prospectively performed 24-h HRV monitoring in 60 patients with electrical burns, those with other major burns, those with other minor burns, and 10 healthy participants. Analysis of HRV in the time and frequency domain was performed.

Results

The difference in sympathetic nerve measures (standard deviation of NN intervals [SDNN], total power [TP] and a low frequency [LF] band) and parasympathetic nerve measures (Root mean square successive difference [RMSSD], the number of interval differences of successive NN intervals greater than 50 ms [NN50], the percentage of differences between following RR intervals greater than 50 ms [pNN50] and a high frequency [HF] band) in patients with burns was significantly decreased during the daytime and the nighttime. the difference in parasympathetic nerve measures were more significantly decreased during the nighttime compared with measures of HRV in healthy participants. The groups of other burns showed significantly lower HRV than the electrical burn group indexed by a very low frequency (VLF) measure and TP during the daytime.

Conclusion

We hypothesized that HRV is a surrogate for autonomic nervous system dysfunction in patients with burns. The patients with burns were observed a sympathetic predominance during daytime and a decreased parasympathetic activity during nighttime. These results of patients with other major burns were more predominant compared with the results of patients with other groups.


IV. Representative measurements of HRV

Table 3 gives representative values of HRV measurements, obtained using the HRV toolkit from the 72 subjects in the MIT-BIH Normal Sinus Rhythm Database and the Normal Sinus Rhythm RR Interval Database. These representative values (comparable to those previously reported in healthy subjects) are not, however, intended as a standard normative database.

Table 3: HRV in 24-hour NN interval time series from 72 ostensibly healthy subjects
(35 males, 37 females, ages 20-76 years, mean age 55)

MeasurementMean ± SD
AVNN (msec)787.7 ± 79.2
SDNN (msec)136.5 ± 33.4
SDANN (msec)127.2 ± 35.7
SDNNIDX (msec)51.2 ± 14.2
rMSSD (msec)27.9 ± 12.3
pNN20 (%)34.2 ± 13.7
pNN50 (%)7.5 ± 7.6
TOTPWR (msec 2 )21490 ± 11577
ULF PWR (msec 2 )18128 ± 10109
VLF PWR (msec 2 )1900 ± 1056
LF PWR (msec 2 )961 ± 722
HF PWR (msec 2 )501 ± 847
LF/HF ratio2.7 ± 1.3

HRV measurements obtained for each of these 72 subjects are available here.


The ultra-low-frequency (ULF) band (𢙀.003 Hz) requires a recording period of at least 24 h (12) and is highly correlated with the SDANN time-domain index (44). While there is no consensus regarding the mechanisms that generate ULF power, very slow-acting biological processes are implicated. Circadian rhythms may be the primary driver of this rhythm (12). Core body temperature, metabolism, and the renin𠄺ngiotensin system operate over a long time period and may also contribute to these frequencies (11, 45). There is disagreement about the contribution by the PNS and SNS to this band. Different psychiatric disorders show distinct circadian patterns in 24 h HRs, particularly during sleep (46, 47).

The VLF band (0.0033𠄰.04 Hz) requires a recording period of at least 5 min, but may be best monitored over 24 h. Within a 5-min sample, there are about 0� complete periods of oscillation (9). While all low values on all 24 h clinical HRV measurements predict greater risk of adverse outcomes, VLF power is more strongly associated with all-cause mortality than LF or HF power (48�). The VLF rhythm may be fundamental to health (12).

Low VLF power has been shown to be associated with arrhythmic death (44) and PTSD (52). Low power in this band has been associated with high inflammation in several studies (53, 54). Finally, low VLF power has been correlated with low levels of testosterone, while other biochemical markers, such as those mediated by the hypothalamic–pituitary�renal axis (e.g., cortisol), have not (55).

Very-low-frequency power is strongly correlated with the SDNNI time-domain measure, which averages 5 min standard deviations for all NN intervals over a 24-h period. There is uncertainty regarding the physiological mechanisms responsible for activity within this band (10). The heart’s intrinsic nervous system appears to contribute to the VLF rhythm and the SNS influences the amplitude and frequency of its oscillations (12).

Very-low-frequency power may also be generated by physical activity (56), thermoregulatory, renin𠄺ngiotensin, and endothelial influences on the heart (57, 58). PNS activity may contribute to VLF power since parasympathetic blockade almost completely abolishes it (59). In contrast, sympathetic blockade does not affect VLF power and VLF activity is seen in tetraplegics, whose SNS innervation of the heart and lungs is disrupted (11, 60).

Based on work by Armour (61) and Kember et al. (62, 63), the VLF rhythm appears to be generated by the stimulation of afferent sensory neurons in the heart. This, in turn, activates various levels of the feedback and feed-forward loops in the heart’s intrinsic cardiac nervous system, as well as between the heart, the extrinsic cardiac ganglia, and spinal column. This experimental evidence suggests that the heart intrinsically generates the VLF rhythm and efferent SNS activity due to physical activity and stress responses modulates its amplitude and frequency.


Result datasets have been posted for HRV analysis. Datasets are keyed on nsrrid .

Individual CSV files are available with R-points for each heartbeat. These annotations were reviewed by a trained technician after exporting from the Compumedics Somte software. ECG was sampled at 125 Hz in SHHS1 and 250/256 Hz in SHHS2. The Somte software outputs sampling numbers assuming 256 Hz, however the rpointadj column has been added to provide an adjusted sample number based on the actual sampling rate of the recording of interest.


What Do HRV Values Mean?

Measuring HRV on a machine and understanding what these HRV measurements mean are two entirely different tasks. With that in mind, let’s break down the intricacies of this measurement in more detail.

“Heart rate variability is based on a set of numbers generated from the times between heartbeats” says Stein, or the periods of time that occur between two beats. These are also known as RR intervals and are measured in milliseconds. The RR interval refers to the time between two peaks on a traditional ECG heartbeat waveform.

Additionally, NN intervals refer to the intervals between two normal R-peaks. In practice, RR intervals and NN intervals are the same, and the use of NN intervals merely emphasizes that normal R peaks, like the one pictured below, were used.

Another term worth remembering during HRV analysis is the root mean square of standard deviation, or RMSSD for short. The root mean square of standard deviation is simply the successive differences between neighboring RR intervals. According to researchers Fred Shaffer and J. P. Ginsberg, we can calculate this “by first calculating each successive time difference between heartbeats in milliseconds. Then, each of the values is squared and the result is averaged before the square root of the total is obtained.”

Methods by which we can analyze HRV data and extract this information exist in either the time-domain or frequency-domain. Each method of analysis is different, but both contain a plethora of information.

Time-domain analysis, for example, is often used in clinical applications of HRV and is the simplest method of HRV analysis. Time-domain analysis utilizes interbeat intervals to measure NN intervals and RMSSD.

Frequency-domain techniques, on the other hand, “are performed on the interbeat interval signal, a plot of the RR intervals versus time or beat number. It is very important with frequency-domain techniques that the data points be equidistant,” according to Harvard Bioscience.


IV. Representative measurements of HRV

Table 3 gives representative values of HRV measurements, obtained using the HRV toolkit from the 72 subjects in the MIT-BIH Normal Sinus Rhythm Database and the Normal Sinus Rhythm RR Interval Database. These representative values (comparable to those previously reported in healthy subjects) are not, however, intended as a standard normative database.

Table 3: HRV in 24-hour NN interval time series from 72 ostensibly healthy subjects
(35 males, 37 females, ages 20-76 years, mean age 55)

MeasurementMean ± SD
AVNN (msec)787.7 ± 79.2
SDNN (msec)136.5 ± 33.4
SDANN (msec)127.2 ± 35.7
SDNNIDX (msec)51.2 ± 14.2
rMSSD (msec)27.9 ± 12.3
pNN20 (%)34.2 ± 13.7
pNN50 (%)7.5 ± 7.6
TOTPWR (msec 2 )21490 ± 11577
ULF PWR (msec 2 )18128 ± 10109
VLF PWR (msec 2 )1900 ± 1056
LF PWR (msec 2 )961 ± 722
HF PWR (msec 2 )501 ± 847
LF/HF ratio2.7 ± 1.3

HRV measurements obtained for each of these 72 subjects are available here.


What are R-R intervals?

Heart rate variability (HRV) is NOT heart rate. Heart rate is just your average number of heartbeats, while HRV is the tiny difference BETWEEN each heartbeat.

HRV is a powerful biomarker that has helped millions to:

  • Improve training and recovery
  • Get fitter, faster and stronger
  • Get healthier and address chronic disease
  • Manage stress
  • And much more!

Key to the power of HRV is its deep connection to your nervous system, which controls and responds to many important body processes, like:

  • Digestion
  • Respiration
  • Metabolism
  • Vision, hearing and smell
  • Sexual function
  • and more

This connection requires tracking small changes (milliseconds) in the intervals between successive heartbeats (Fig 1), also called "RR intervals". This is different from heart rate, which just averages the number of beats per minute.

Fig 1: Heart rate variability

To measure HRV, you must have a compatible heart rate monitor that can accurately sense RR-intervals. Many monitors do not meet this standard because:

  • They were not designed with HRV in mind, and do not have the required accuracy. Most wrist-wearables (watches, etc) fall into this category.
  • They record RR-intervals but artificially "smooth", average or alter them before transmitting to us. Our app tries to alert you when this happens with a reading.

For example, most wrist bands and watches have difficulty sorting through the "noise" of an HRV reading because of all the complicated tissue in your wrist that surrounds your arteries.

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Heart rate variability metrics for fine-grained stress level assessment

This study evaluates which HRV metrics allow to distinguish the mental stress levels.

We applied the Trier Social Stress Test (TSST) method, a standardized stress-inducing protocol, to 14 volunteers.

We measured the ECG signal with a wearable vital signal monitoring device with Medical Device certification.

We evaluated HRV metrics in time and frequency domain and non linear analysis in various short-time scales.

Wearable device and AVNN using 50 s of windows length analysis, allows a fine-grained analysis of stress effect as an index of psychological stress.


The ultra-low-frequency (ULF) band (𢙀.003 Hz) requires a recording period of at least 24 h (12) and is highly correlated with the SDANN time-domain index (44). While there is no consensus regarding the mechanisms that generate ULF power, very slow-acting biological processes are implicated. Circadian rhythms may be the primary driver of this rhythm (12). Core body temperature, metabolism, and the renin𠄺ngiotensin system operate over a long time period and may also contribute to these frequencies (11, 45). There is disagreement about the contribution by the PNS and SNS to this band. Different psychiatric disorders show distinct circadian patterns in 24 h HRs, particularly during sleep (46, 47).

The VLF band (0.0033𠄰.04 Hz) requires a recording period of at least 5 min, but may be best monitored over 24 h. Within a 5-min sample, there are about 0� complete periods of oscillation (9). While all low values on all 24 h clinical HRV measurements predict greater risk of adverse outcomes, VLF power is more strongly associated with all-cause mortality than LF or HF power (48�). The VLF rhythm may be fundamental to health (12).

Low VLF power has been shown to be associated with arrhythmic death (44) and PTSD (52). Low power in this band has been associated with high inflammation in several studies (53, 54). Finally, low VLF power has been correlated with low levels of testosterone, while other biochemical markers, such as those mediated by the hypothalamic–pituitary�renal axis (e.g., cortisol), have not (55).

Very-low-frequency power is strongly correlated with the SDNNI time-domain measure, which averages 5 min standard deviations for all NN intervals over a 24-h period. There is uncertainty regarding the physiological mechanisms responsible for activity within this band (10). The heart’s intrinsic nervous system appears to contribute to the VLF rhythm and the SNS influences the amplitude and frequency of its oscillations (12).

Very-low-frequency power may also be generated by physical activity (56), thermoregulatory, renin𠄺ngiotensin, and endothelial influences on the heart (57, 58). PNS activity may contribute to VLF power since parasympathetic blockade almost completely abolishes it (59). In contrast, sympathetic blockade does not affect VLF power and VLF activity is seen in tetraplegics, whose SNS innervation of the heart and lungs is disrupted (11, 60).

Based on work by Armour (61) and Kember et al. (62, 63), the VLF rhythm appears to be generated by the stimulation of afferent sensory neurons in the heart. This, in turn, activates various levels of the feedback and feed-forward loops in the heart’s intrinsic cardiac nervous system, as well as between the heart, the extrinsic cardiac ganglia, and spinal column. This experimental evidence suggests that the heart intrinsically generates the VLF rhythm and efferent SNS activity due to physical activity and stress responses modulates its amplitude and frequency.


Result datasets have been posted for HRV analysis. Datasets are keyed on nsrrid .

Individual CSV files are available with R-points for each heartbeat. These annotations were reviewed by a trained technician after exporting from the Compumedics Somte software. ECG was sampled at 125 Hz in SHHS1 and 250/256 Hz in SHHS2. The Somte software outputs sampling numbers assuming 256 Hz, however the rpointadj column has been added to provide an adjusted sample number based on the actual sampling rate of the recording of interest.


What are HRV score, RMSSD, ln(RMSSD), SDNN and PNN50?

Our HRV score is based on RMSSD and represents the strength of your Autonomic Nervous System (specifically the Parasympathetic branch) at a given time. Click here for more information on average HRV scores in our community.

The other numbers provided are for reference for those interested in more detailed analysis of their HRV data. Most app users will work solely with the HRV score and Readiness score, however, those working with a coach or practitioner may need the other metrics as described below.

Here’s a quick run down of what they mean (NN or R-R intervals means the time between two successive heart beats):

RMSSD: Root mean square of the successive differences – used for a good snapshot of the Autonomic Nervous System’s Parasympathetic branch and is the basis of our “HRV Score”

  • RMSSD is strongly backed by research and is considered the most relevant and accurate measure of Autonomic Nervous System activity over the short-term. Here are a few studies referencing its use:

ln(RMSSD): A natural log is applied to the RMSSD in order to distribute the numbers in an easier to understand range

SDNN: Standard deviation of the NN (R-R) intervals

NN50: The number of pairs of successive NN (R-R) intervals that differ by more than 50 ms.

PNN50: The proportion of NN50 divided by the total number of NN (R-R) intervals.

Please see this article for an overview of what these metric correlate to: HRV Metrics and Norms

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Abstract

The quality of life and individual well-being are crucial factors in disease prevention. Particularly, healthy lifestyle lessens the risk and occurrence of main diseases, such as cardiovascular diseases and metabolic disorders. Since a patient has an active role in being a co-producer of his/her health, innovative devices and technologies have been devoted to helping folks in self-evaluation and expected to play a key role to maintain their well-being. In this work, we present a very promising assessment tool for health, Heart Rate Variability (HRV). HRV is the difference in time between one heartbeat and the next. HRV measurement is simple and non-invasive, it is derived from recording of electrocardiogram (ECG) on free-moving subjects. The main aim of this work is to investigate the dynamics in the autonomic regulation of the heart rate by using frequency and temporal analysis to correlate between the HRV and these physiological patterns.

In addition to the applied frequency and temporal analyses, pattern recognition is also accomplished using Neural Networks which are further implemented and explored in this work. In the first place, the detection of the sleep/awake states is achieved. Next, a multiclassification of different types of activities such as sleeping, walking, exercising and eating is performed.


Background

Heart rate variability (HRV) is a noninvasive method used to quantify fluctuations in the time interval between normal heart beats. The purpose of this study was to compare the autonomic nervous system functioning of patients with burns to healthy participants after their burn scars had been re-epithelialized.

Materials and methods

The authors prospectively performed 24-h HRV monitoring in 60 patients with electrical burns, those with other major burns, those with other minor burns, and 10 healthy participants. Analysis of HRV in the time and frequency domain was performed.

Results

The difference in sympathetic nerve measures (standard deviation of NN intervals [SDNN], total power [TP] and a low frequency [LF] band) and parasympathetic nerve measures (Root mean square successive difference [RMSSD], the number of interval differences of successive NN intervals greater than 50 ms [NN50], the percentage of differences between following RR intervals greater than 50 ms [pNN50] and a high frequency [HF] band) in patients with burns was significantly decreased during the daytime and the nighttime. the difference in parasympathetic nerve measures were more significantly decreased during the nighttime compared with measures of HRV in healthy participants. The groups of other burns showed significantly lower HRV than the electrical burn group indexed by a very low frequency (VLF) measure and TP during the daytime.

Conclusion

We hypothesized that HRV is a surrogate for autonomic nervous system dysfunction in patients with burns. The patients with burns were observed a sympathetic predominance during daytime and a decreased parasympathetic activity during nighttime. These results of patients with other major burns were more predominant compared with the results of patients with other groups.



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