1.5 HRV Time Domain Measures
HRV Course: Part 1 concludes with an in-depth look into time-domain measures of HRV and how they can be interpreted.
- 0:00 - 3:06 Time Domain Measures
- 3:07 - 5:06 Interpreting Time Domain Measures
- 5:07 - 7:00 Session recap
This video concludes Part 1 of our HRV Masterclass. Check out Labfront Academy for all of our courses.
- Now we have a little bit of time to quickly go through the time domain. And as I mentioned before, we're not focusing on the RR interval, but the n and r interval interval, the normal rhythms that are associated with the SA nodes.
- And the four big ones that I want you to pay attention with, there are various others, but the ones that count for the majority of time domain measures in the literature out there are these four, the standard deviation of and intervals, the standard deviation, or five minute averages of the intervals, the root mean square of successes and intervals, differences. And then PNN 50, which is the percentage of an intervals that are greater than 50 mil of seconds different than its prior in intervals. And each of these sort of require a certain amount of time for the RMS SD, pn and 50, you can use up to five minutes, some have used as little as one minute for msst, for standard deviation of an interval, and SDA and n, I think a lot of them give us 24 hours, and the clinical significance that we'll talk about a little bit later.
- So if you take a look at the NN interval, over time, you can see a lot of this variability. And the two of the four time domain measures takes a look instead at the successive differences in the intervals. And it's basically difference between the RNN interval compared to the prior one. And this is how it looks like here. So if it if your heart rate was about 50 beats per minute on one time, and then it subsequently was, or let's just say it was 5080 milliseconds for the first internet fall, then it was 90 seconds on the subsequent interval, then you have a 10 millisecond difference between the two.
- That's what they use in order to put out this time series. And you could see that this time series, it eliminates a lot of this slower dynamics that you see in the NN interval. And you see a lot of the faster process that occurs. And so armet MSSD, which is the root mean squared of SS of this graph here, and PNN 50, which is it number of times that you have the successive differences, you'll either above or below these thresholds are an indication of these more rapid events, because you lose the a lot of the slower components of your heart variability. And when you evaluate the successive differences in intervals. So when you're dealing with these measures, you're dealing with a faster components of your heart rhythm. And so you're talking about high frequency, heart rate variability, or the respiratory sympathy, arrhythmias, which we recall is largely a parasympathetic system phenomenon.
- So the question is, what are the physiological processes most correlated with SDN or the standard deviation of the end interval? And the answer is, it really depends on the duration of the data for two minutes, because we're doing predominantly with high frequency information or variability, the standard deviation s dnn reveals or represents much more RSA or parasympathetic nervous activity.
- For five minutes, we are invoking both the low frequency and high frequency components. So you have both a bearer reflex and the RSA physiological processes within that signal. And then when you're talking about the 24 hour, STN, it is predominant, aided by much slower processes. And you can see that by the spectral power of each of these frequency ranges, you can see that high frequency low frequency generally has lower power. And so for the lower frequencies, the very low and ultra low frequencies, they have a much higher power representation in this power spectrum. So when you're obtaining a 24 hour standard deviation, a lot of the variability is accounted by the slower rhythms and so Estienne have 24 hours represents the circadian or slower hormonal processes. So in summary, these two measures which rely on the successive and then intervals are MSSD, pn and 50 represent high frequency heart rate variability, or respiratory sinus arrhythmia, and so indicates much more of a parasympathetic nervous activity.
- As dnn depends on the duration is shorter that it is there It's more correlated with the higher frequency ranges. And then SDA and then here associates more with the circadian rhythm. So that is basically the end of the part one of this heart rate variability Deep Dive.
- To summarize, the 1970s and 80s saw the major advances in heart rate variability, physiology and measures at rest, parasympathetic nervous stone normally is greater than the sympathetic nervous system tone. And Heart Rate Variability frequency domain, for each frequency range is associated with a specific physiological process. High frequency is associated with RSA or the parasympathetic nervous system. lower frequency is associated with bear reflex and basic monitor which has both a parasympathetic and sympathetic component. And the ultra low frequency deals with hormone or circadian rhythms. And high frequency Heart Rate Variability correlates with vagal current sympathetic nervous system and the faster CNS activity is filtered out at the SA node.
- The time domain measures are MSSD of pain and 50 correlate with high frequency high heart rate variability, and therefore represents are represented by the parasympathetic nervous system. And SDN depends on the duration of the time series analysis.
- Now, because there's so much information here, there's probably going to be two or three more subsequent talks on on heart rate variability. And some of the things that I'll talk about in the future will be the clinical studies, some of the physiological tests that are used to assess the autonomic nervous system, nonlinear measures and some of the applications of heart rate variability in psychological situations, such as stress and, and and sleep as well.