Introduction_to_HRV

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HRV4TRAINING

HEART RATE VARIABILITY: A PRIMER MARCO ALTINI
(HTTP://MARCOALTINI.COM)
(HTTP://WHOMWE (W/) .HRAPVP4(/ATPRP.HATMINL) INGF.ACQO(/FAMQ.H/TBMLL)OG/PHRIVEAACYR&TTE-RRMAS (T/PERIV-ACY--TERMS.HTML)Data Scientist, PhD
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candidate, Developer,
Passionate Runner.

5/6/2015 0 Comments (http://www.hrv4training.com/blog/heart-rate-variability-a-primer#comments)

This is the first post on a new blog I'm starting here on the HRV4Training website, where I will write
about HRV, hardware/sensors, PPG, data acquired using HRV4Training on me as well as on the
growing community of users, and so on. I will try to post data as well as much as possible, so that
you can reproduce most of the things blogged here. 

Let's get started with the basics. Follow @marco_alt

WHAT IS HEART RATE VARIABILITY (HRV)? Register to the HRV4Training
mailing list
Each beat of our heart is triggered by an electrical impulse that can be easily recorded by an
electrocardiogram (ECG), one of the most common ways to monitor heart activity. However, our (http://www.hrv4training.co
heart doesn't beat at a constant frequency.  When we talk about heart rate variability (HRV), we m/mailing-list.html)
are interested in capturing the variability that occurs between heart beats. and try the app

Let's look at 60 seconds of ECG data. This is some data I recorded on myself using the ECG (https://itunes.apple.com/us
Necklace, a research prototype we developed when I was working at imec, a few years ago. The /app/hrv4training/id6869239

device is a small sensor connected to 2 ECG leads. Here I haven't converted the values on the y 70?ls=1&mt=8)!
axis to mV , but anyways we are more interested in looking at what happens on the x axis (you
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can click on the image to enlarge):
HRV Measurements Best
(/uploads/1/3/2/3/13234002/223486_orig.png?750) Practices
In technical jargon, the differences between beats are called RR intervals. The name derives from
the fact that the shape of the ECG signal at each beat has been assigned letters (namely the QRS 1. Context & time of the day
complex). For more on the QRS complex you can just have a quick look on the Wikipedia page (http://www.hrv4training.co
(http://en.wikipedia.org/wiki/Electrocardiography), however the only relevant point here is that R m/blog/hrv-measurements-
represent the peak(s). Going back to my ECG, if we zoom in a bit and look at only 10 seconds, we
can see clearly there are differences between intervals; some are shorter, other longer: context-time-of-the-day)
2. Duration

(http://www.hrv4training.co
m/blog/hrv-measurements-

duration)
3. Test type
(http://www.hrv4training.co
m/blog/orthostatic-hrv-
measurements-training-and-

recovery)
4. Paced breathing
(http://www.hrv4training.co
m/blog/hrv-measurements-
paced-breathing)

Data Analysis
1. Acute Changes in HRV
(http://www.hrv4training.co
m/blog/acute-changes-in-

heart-rate-variability)

(/uploads/1/3/2/3/13234002/3696889_orig.png?768) Camera & Sensors
1. ECG vs Polar & Mio Alpha
Another way we can look at this differences is by plotting an histogram of the RR intervals. (http://www.hrv4training.co
Basically we stack up RR intervals that are of similar duration. This way it's actually much easier to m/blog/hardware-for-hrv-

what-sensor-should-you-

see how the values are distributed over a rather wide range. For this plot, I used again the full use)

minute of datHaOfrMoEm(/)the firsAtPpPlo(/tA:PP.HTML) FAQ (/FAQ.HTML) PRIVACY & TERMS (/PRIVACY--TERMS.HTML)2. Camera vs Polar
(http://www.hrv4training.co

BLOG (/BLOG.HTML) CONTACT (/CONTACT.HTML) MAILING LIST (/MAILING-LIST.HTML) m/blog/heart-rate-

variability-using-the-phones-

camera)

(/uploads/1/3/2/3/13234002/9458000_orig.png?449) Other
1. Intro to HRV
For this minute of data, RR interval values range between 832 and 1094 milliseconds. We can (http://www.hrv4training.co
compute the instantaneous heart rate simply as: m/blog/heart-rate-
variability-a-primer)
2. HRV normal values
(http://www.hrv4training.co
m/blog/heart-rate-
variability-normal-values)

HR = 60 × 1000
RR

 The instantaneous heart rate in this case gives us a range between 55 and 72 beats per minute.
So far so good, now, why do we care about these differences in RR intervals?

AUTONOMIC REGULATION

The autonomic nervous system (ANS) controls and regulates many functions of our body, from the
heart beating to respiration. The ANS is in control of how our body reacts to stressors, or in
other words, the fight or flight response. We typically think of the ANS in the context of its two
complementary branches, the sympathetic and parasympathetic nervous systems, that
continuously regulate the ANS by acting in different directions. While the sympathetic nervous
system is responsible for stimulating the body's fight or flight response, the parasympathetic
nervous system is mainly responsible for the body's resting functions.

The cardiovascular system is mostly controlled by autonomic regulation through the activity of
sympathetic and parasympathetic pathways of the ANS and analysis of HRV permits analysis in
this control mechanism [1]. Several studies on HRV highlighted how different features can
provide insights in autonomic regulation, sympathetic and parasympathetic activity. In particular, a
large body of studies showed a strong link between certain time and frequency domain HRV
features, and parasympathetic activity, while findings on sympathetic activity are a bit more
inconclusive.

Simply put, monitoring parasympathetic activity via HRV can provide insights on
physiological stress, with higher level of stress resulting in lower HRV. For example, in the context
of sports, heavy training is responsible for shifting the cardiac autonomic balance toward a
predominance of the sympathetic over the parasympathetic drive. This means that heavy
training will reduce HRV and by monitoring HRV we can possibly optimize training, reduce the
risk of overtraining and ultimately improve performance.

On these last points, many research studies showed that short-term changes in HRV features,
used to assess training load and recovery, are a reliable measure of parasympathetic activity [2, 3]
and can even be user to guide training plans [4].

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thought of as an almost instantaneous value, HRV requires a certain amount of data to be
accumulated, before it can be computed.

Clinical practice recommends 5 minutes of data to be used for features extraction, however in the
recent years more and more work was able to show that much shorter windows provide
equivalence, and more practical 60 seconds recordings are sufficient [5], especially when we look
at time domain features.

Another important aspect to take into account is pre-processing to perform on RR intervals before
we compute features. One of the most important steps is RR-Intervals correction, which
prevents artifacts due to ectopic beats or motion from affecting features computation, as often
reported in literature for HRV analysis. 

It is advised to keep RR interval correction to 20%, meaning that every RR interval which differs by
more than 20% from the previous one, will be discarded. Once beats have been discarded, we
refer to them as NN intervals, since they are now including only "normal" values. 

HRV4Training uses a configurable time window, so that you can go up to 5 minutes if you want to,
but lets you also take shorter 60 seconds measurements. Additionally, RR interval correction is
always performed after the recording, before computing features. 

HRV FEATURES

HRV features can be grouped into two main categories, time and frequency domain features. Here
is a simple diagram of the procedure that leads to features extraction for both time and frequency
domain features:

(/uploads/1/3/2/3/13234002/7639403_orig.png?292)

Frequency domain features require a bit more signal processing, since NN intervals are not at a
constant frequency (that is the whole point), and therefore they need to be interpolated before we
can do Fourier analysis.

TIME DOMAIN FEATURES

The most commonly used time domain features are AVNN (mean of the NN intervals), SDNN
(standard deviation of NN intervals), rMSSD (square root of the mean squared difference of
successive NN intervals) and pNN50 (number of pairs of successive NN intervals that differ by
more than 50 ms). Here is how to compute them given an array of NN intervals of k elements:

AVNN, mean of NN intervals:

AV NN = 1 n
n
∑ NNk

k=1

SDNN, standard deviation of NN intervals:

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where AVNN is computed as above.
rMSSD, square root of the mean squared difference of successive NN intervals:

rMSSD = ⎷−n−−−1−1−−∑nk−=−−11−(N−−N−k−+−1−−−−N−−N−k−)−2

pNN50, number of pairs of successive RRs that differ by more than 50 ms
The difference between beats is calculated as above:

NNk+1 − NNk

Then, if n50 is the number of beats for which we have a difference greater than 50 ms (
(NNk+1 − NNk) > 50 ), pNN50 is computed as:

pNN50 = n50 100
n

FREQUENCY DOMAIN FEATURES

Frequency domain features can be computed in many different ways, often making it very difficult
to compare results between different studies. For transparency, here is how HRV4Training
computes HRV frequency domain features:

• collect RR intervals in a time window (I keep all this values in milliseconds)
• remove RR intervals differing more than 20% from the one preceding them (i.e. RR

intervals correction)
• interpolate NN intervals at 4Hz (so 1 point every 250 ms, this is necessary since NN

intervals are not evenly spaced in time, and we need evenly spaced data in order to
perform frequency domain analysis)
• remove the DC component
• at this point I convert into seconds
• compute hamming windowing on the time series I've got from previous steps
• perform FFT
• compute frequency power for the 2 bands of interest (LF and HF).

If any of these steps differs, you'll get different values. Also, sometimes frequency domain features
are expressed in terms of relative power and that is again different. I find it hard to directly
compare these features to what you can find in literature, but maybe giving the series of steps will
help.

CLOSING THE LOOP

In the previous sections we talk about how HRV features can be representative of autonomic
regulation, and in particular of 
parasympathetic activity. We also talked about how certain features are more representative of
parasympathetic activity and how a decrease in HRV features values can be indicative of higher
physiological stress. 

HRV4Training reports all features, which can be helpful if you want to run your analysis or are an
expert in HRV, but can be confusing at first impact. Many of these features are actually trying to
measure the same thing (i.e. parasympathetic activity) and therefore will provide values that are

highly correlated over time. 

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According to literature, the most useful features are rMSSD and HF. However, more challenges

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in the previous section, computing frequency domain features seems to be "less standardized".

For these reasons it's typically a good idea to look at rMSSD as a marker of parasympathetic

activity, rather stable during short term recordings. I will cover other important aspects (breathing

rate, time and consistency of the test) in future blog entries.

In the next post, I will cover hardware (sensors) that can be used to acquire RR intervals data and
alternatives such as using the iPhone's Camera and PPG, a feature that is unique to
HRV4Training.

Follow @marco_alt

REFERENCES

[1] Aubert, André E., Bert Seps, and Frank Beckers. "Heart rate variability in athletes." Sports Medicine 33.12 (2003): 889-919.
[2] Garet, Martin, et al. "Individual interdependence between nocturnal ANS activity and performance in swimmers." Medicine and science in sports and
exercise 36 (2004): 2112-2118.
[3] Pichot, Vincent, et al. "Relation between heart rate variability and training load in middle-distance runners." Medicine and science in sports and
exercise 32.10 (2000): 1729-1736.
[4] Kiviniemi, Antti M., et al. "Endurance training guided individually by daily heart rate variability measurements." European journal of applied physiology 101.6 
(2007): 743-751.
[5] Esco, M. R., & Flatt, A. A. (2014). Ultra-Short-Term Heart Rate Variability Indexes at Rest and Post-Exercise in Athletes: Evaluating the Agreement with Accepted 
Recommendations. Journal of sports science & medicine, 13(3), 535.

DATA
Here are the two files used for this blog post (60 seconds of ECG and RR intervals):

(/uploads/1/3/2/3/13234002/ecg_60seconds.csv)

ecg_60seconds.csv

Download File (/uploads/1/3/2/3/13234002/ecg_60seconds.csv)

(/uploads/1/3/2/3/13234002/rr_60seconds.csv)

rr_60seconds.csv

Download File (/uploads/1/3/2/3/13234002/rr_60seconds.csv)

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SDNN = √−n1−−∑k−=n−1(−N−−N−k−−−−A−−V−N−−N−)−2