Heart rate variability (HRV) is a relatively novel cardiovascular indicator that recently began its path to widespread amateur use without direct medical intervention. This impulse to progress was caused by the influence of the global trend of a data-driven digital healthy lifestyle. And, of course, consequent rapid influx of both – the various hardware health rate variability monitors as well as companion smartphone apps to measure HRV. Logically, ever since the tech had advanced enough so that no one, for example, needs to enter blood pressure and heart rate, – the boom of 24/7 data-driven health advice was doomed to happen.
Indeed, with such an increase in popularity, the boost of the public’s good HRV awareness was practically inevitable as well. As a result, the answers to simple questions like “what is HRV” and “how to improve heart rate variability” are becoming more and more evident to the end-users. It is now accessible for us to choose the most perfect Apple Watch stress app to use routinely. The best HRV app is now easily selected by user-friendliness because many of the back-end algorithms are essentially the same.
Taking this into mind, I have decided to write a more in-depth article specifically regarding the statistical inside of the heart rate variability. This information is, of course, not a necessity to know, but it is valuable enough to open your mind wider and deepen your understanding of HRV concepts. Understanding the phenomenon of heart rate variability is especially important when you are trying but do not know how to reach flow state. Some of the content was taken and remade from the article about HRV that was written by specialists at Welltory. By the way, if you need an excellent HRV wrap-up, do not hesitate to take your time to read that article as well.
Methods of mathematical analysis
Heart rate variability analysis is a new methodology for studying the processes of regulation of physiological functions, where the circulatory system is considered an indicator of the adaptive reactions of the whole organism.
In the international standards proposed in 1996, two groups of mathematical analysis of HRV are distinguished: in the time domain and the frequency domain.
Time-domain methods
These methods are based upon the analysis of the beat-to-beat (NN) intervals, with the extraction of different heart rate variables either by statistical or geometrical models.
Statistical methods
These crucial parameters are most often calculated:
- SDNN – standard deviation of NN intervals. This parameter is most commonly calculating for the last 24 hours. A derivative exists – SDANN – which calculates the standard deviation for short periods, usually up to 5 minutes. SDNN shows all the cyclic statistical components, and it represents overall variability with corresponding physiological conclusions.
- RMSSD – “root mean square of the successive differences” – is the square root of the mean of the squares of successive differences between adjacent NN. This parameter, thus, gives a better idea of changes in dynamic. A logically similar parameter is SDSD – “standard deviation of successive differences.”
- NN50 – number of consecutive NN intervals that differ by more than 50 ms. An integral derivative is pNN50 – a proportion of such gaps to all of them. You can conclude that pNN50 basically reflects the probability that each randomly selected interval will differ from the average by more than 50 ms.
Additionally, there are self-explanatory parameters NN20 and pNN20.
Geometric methods
A normalized chronocardiogram (sequence of NN intervals) can be displayed as a particular geometric structure in accordance with international standards. The parameters are then measured and used as integral characteristics of the initial chronocardiogram.
When working with geometric methods, there are three main approaches:
- Basic measurements of the geometric model are converted according to a certain set of formulae into HRV characteristics;
- A geometric model is interpolated in a certain mathematical way, and then the coefficients describing this mathematical form are analyzed;
- The geometric shape is classified, several categories of geometric samples are distinguished, representing different classes of HRV (elliptical, linear, Lorenz curve).
A distribution histogram of interval times is also drawn. The following parameters are usually used to describe the histogram: AMO – amplitude of the histogram mode, MO – histogram mode, SD – standard deviation; less often – asymmetry (Ass), excess (Ex), variation range (dX), coefficient of variation (V), etc.
Frequency-domain methods
Frequency domain techniques are used to count the number of NN intervals that correspond to each frequency band. The standards recommended to distinguish between the following frequency bands (components):
- High frequency (HF) – 0,15-0,4 Hz – are induced by the parasympathetic nervous system (PSNS). Thus the HF value represents PSNS tonus as a direct proportion. Therefore, the higher – the better.
- Low frequency (LF) – 0,04-0,15 Hz – are induced by the sympathetic nervous system (SNS). Thus the LF value represents SNS tonus in the same direct proportion. Therefore, the lower – the better.
- Very low frequency (VLF) – 0,003-0,04 Hz – is induced by the humoral regulation (hormones and hormone-like substances) that kicks in once the SNS can’t keep up. Indeed, it is an indicator of severe load upon the organism, and the lower – the better.
- Ultra-low frequency (ULF) – lower than 0,003 Hz – rarely met.
Some apparent derivative ratios are used to improve the evaluation:
- LF/HF – compares the tonuses of PSNS and SNS. Possible results:
- >2 – the SNS is prevalent, and the organism is overly mobilized;
- 1-2 – the systems are in balance;
- <1 – the PSNS is prevalent, and the organism is relaxed.
- HF/LF/VLF – wave balance comparison to understand which system is currently in charge of heart activity. Remember that if VLF is predominant – your body is either exhausted or sick and has trouble coping with stress.
As a conclusion
I hope this information helped you shed some light on the deeper mechanisms of HRV and motivated you to continue getting a more profound understanding of the subject. Stay healthy and rational!