hypoxbreath®- HRV Monitoring
hypoxbreath®
HRV Monitoring
In today’s science, HRV is considered an important indicator of health and fitness. As a marker of physiological resilience and behavioral flexibility, it reflects the body’s ability to effectively adapt to stress and environmental demands. E.g. it is known that heart attack patients have a very low HRV.
Using HRV evaluation, it is therefore possible to wonderfully understand the effect of a measure or circumstance on the physiological processes in the body.
All the information listed here is intended to provide an initial understanding of a highly complex topic area of “HRV” and the possibilities of the evaluation parameters of the hypoxbreath® advanced device. Please do not use this information for general diagnoses.
The integrated HRV analysis of the hypoxbreath® advanced is one of the most powerful permanently integrated IHHT/IHT HRV tools worldwide. It combines both linear (time-related) and non-linear (frequency-related) analyses of the common HRV parameters that we consider important for successfully assessing an IHHT/IHT training session with an optimally set training stimulus. The default integrated HRV test also allows a qualitative resting HRV measurement.
The HRV analysis of the hypoxbreath® has been developed over the last 5 years by an experienced development team of medical physicists and technicians as well as engineers. Our HRV software algorithms are based on scientific standards.
hypoxbreath® compiles the training assessments easily and clearly:
The software of the hypoxbreath® advanced
was extensively updated in 2023 and now includes*:
Linear (time-related), non-linear and frequency-related HRV parameters:
- Mean RR in milliseconds and mean HR
- RMSSD
- SD1 and SD2
- RR Response (RR Distribution) / Detrended RR Series
- Stress index
- Detrended fluctuation analysis (DFA)
- Pointcare and Lorenz plot (SD1, SD2 and SD2/SD1)
- Power spectrum
* only applies to the hypoxbreath advanced device series. Models with app control and integrated HRV monitoring display the RMSSD and stress index parameters.
THIS MAKES OUR HYPOXBREATH® ADVANCED IHHT/IHT SYSTEMS INTERESTING FOR PROFESSIONAL BIOFEEDBACK TRAINERS.
Interval hypoxia training with or without HRV monitoring?
HRV (heart rate variability) has long been regarded in the medical field as the most reliable parameter for the state of the autonomic nervous system (ANS) and thus for almost all physiological and psychological processes of our body.
Our autonomic nervous system (ANS) is made of two main parts – the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). While the activity of the sympathetic nerve increases the heart rate and reduces the HRV, the parasympathetic nerve lowers the heart rate and thus has a positive influence on the HRV. Good HRV reflects the heart’s ability to adapt to internal and external physical and psychological changes, which equates to a good regulatory capacity of the body.
HRV therefore provides information about the physiological and psychological state of our body long before noticeable and visible symptoms occur.
Example: Derivation of the regulatory capacity of the ANS of a 40-year-old woman based on an HRV curve
Although IHHT/IHT training is performed while lying down or sitting, it puts a strain on the body. Each training application should therefore always be adjusted individually and measured against the current physical and psychological condition of the trainee. This allows training stimuli to be optimally set and training goals to be achieved faster.
The integrated HRV test and HRV monitoring over the entire training session help find the best training parameters for the trainee and to make the training course as effective as possible.
THE FEATURES OF THE HYPOXBREATH® – HRV-MONITORING AT A GLANCE
User friendliness
No additional software or hardware required. The HRV monitor is fully integrated into the software of our IHHT/IHT devices**.
MEASUREMENT DATA OPTIMIZATION
Automatic correction algorithm for correcting measurement artifacts from RR interval time series.
Documentation
Useful reporting and export functions for documenting training successes as a PDF report with HRV analysis parameters and graphics and/or as a CSV export with all analysis data sets (numerical values) for complex long-term analyses
**only applies to the hypoxbreath advanced device series. In the hypoxbreath® and the hypoxbreath®-med, the HRV monitoring contains the phase-related RMSSD value calculation as well as the Stress Index
THE HYPOXBREATH® HRV PARAMETERS
IN DETAIL AND THEIR MEANING
Mean RR in milliseconds
The mean RR duration is considered mathematically, the arithmetic mean over all RR intervals of a measurement in [ms].[ms] RRi denotes the i-th RR interval, and N is the number of all RR intervals of the measurement.
Stress index. It is typically measured during training and interpreted as a measure of the stress intensity.
RMSSD (Root mean square of successive differences)
The RMSSD value shows the change in heart rate between two consecutive heartbeats (i.e. from R to R wave in the ECG). It is considered an indicator of parasympathetic activity.
The diagnostic significance of the RMSSD value as an HRV parameter is greatly underestimated due to its susceptibility to measurement artifacts and arrhythmia, although it serves well as a marker of the body’s ability to recover, and can also be used to assess parasympathetic activity.
It is calculated from the temporal differences between the successive heartbeats, more precisely the R waves of the ECG curve. The changes in heart rate can be used to determine how well the body can switch between stress and relaxation.
A high RMSSD value indicates a strong parasympathetic nervous system and a good ability to regulate the autonomic nervous system (ANS). This means that the body can handle the switch between stress and recovery well.
Use this value in the chronological view of the training sessions to draw conclusions about stress levels and the progressive increase in resilience.
While the RMSSD value from the measurement of a training session is an indicator of the current state of stress, it can also be an indicator of the state of chronic stress in relation to other HRV parameters.
For example, the RMSSD value is often reduced in people with exhaustion, chronic fatigue or sleep disorders, while the stress index is significantly increased.
HRV under stress
HRV of a resilient/resilient body
Average HR (average heart rate/heart rate)
Average HR is usually measured during training. It is a good indicator of the intensity of the load.
Stress index
The calculation of the stress index includes values from HRV and pulse measurement.
For ECG experts: The stress index is based on the frequency distribution (histogram) of measured RR intervals and is calculated from the ratio of the number to the variance of the RR intervals. A small number of identical RR intervals or a large number of different RR intervals (high dispersion) indicate good HRV and vice versa.
The stress index can be used to draw conclusions about the regulatory capacity of the autonomic nervous system (ANS). The higher the stress index, the greater the current stress load (physical/emotional, etc.) and thus the imbalance of the autonomic nervous system (ANS).
PNS and SNS – Regeneration/Stress Index
The subjective perception of stress and real stress loads often differ greatly. In addition, each person reacts very differently to stressful situations. The analysis of the HRV parameters, PNS index and SNS index, helps in the qualitative assessment of the training session. You can draw conclusions about the actual physical load of the training and whether the trainee actually comes to rest during the recovery phases (hyperoxia/normoxia).
The regeneration index (PNS) is calculated from the HRV parameters Mean RR Interval, RMSSD and SD1, i.e. those parameters that map parasympathetic activity. The PNS can be used to draw conclusions about the body’s ability to recover.
A PNS Index of 0 means that the recovery value corresponds to the normal population average.
A PNS Index > 0 indicates a better recovery value.
A PNS index <0 is therefore to be interpreted as below average.(1)
The Stress Index (SNS) is calculated from the HRV parameters Mean Heart Rate, Stress Index and SD2, i.e. those parameters that map sympathetic activity.
An SNS Index > 0 indicates a stress/strain situation.
An SNS index < 0 is to be assessed positively, depending on the training goal.
The Stress Index (SNS) and Regeneration Index (PNS) always assume values between -3 and +3 on average. In a long-term measurement over the course of the day, however, higher values can be achieved if extraordinary stress reactions have occurred. (1) (1)
(1) Source: Mesana https://www.mesana.com/belastungs-und-regenerationsindex-teil-1
RR Response (RR Distribution)
The RR response – as a further linear (time-related) parameter of HRV analysis – is shown in the so-called histogram, a column diagram. This presentation describes the frequency distribution of the different RR intervals (summarized in the columns). I.e. how often did the RR interval R1, the RR interval R2, etc. occur throughout the measurement. Each column on the horizontal axis therefore describes an RR interval with the respective measured frequency on the vertical axis.
Examples of RR histograms:
A – Top left: Healthy, the arrangement of the columns is central, with the highest columns (Mode) in the range of 0.7–1.0 s. Normal cardiac activity usually shows an asymmetrical, cupola-shaped, and dense histogram, similar to a Gaussian curve.
B – Top right: Example of a histogram of a person with arrhythmia
C – Bottom left: Example of a histogram of a person with cardiac insufficiency.
D – Bottom right: Example of a histogram of a person with syncope. This diagram is diverse and there are two distinct peaks. (Attention: In long-term HRV measurements, several peaks/frequency peaks are usually visible in the histogram.)
Source: Diagnostics 2020, 10(5), 322; https://doi.org/10.3390/diagnostics10050322
DFA – “Detrended fluctuation analysis”
DFA is a quantitative method for analyzing time and measurement series, in which the degree of randomness or regularity of a time series is determined. In hypoxbreath HRV monitoring, this method analyses the HRV measurement series according to random and repetitive data. This allows conclusions to be drawn about how the individual control systems (sympathetic and parasympathetic) work together.
The DFA-alpha 1 values are determined using non-linear mathematical methods based on the exact RR intervals between the individual heartbeats. The DFA-alpha value 1 not only says something about the purely quantitative (temporal) changes in HRV, but also about the qualitative factors of regulation. Qualitative considerations of HRV take a somewhat higher priority in diagnostics and therapy.
Alpha 1 values of 1.0 mean that 50% random values were measured in HRV. This indicates a rapid responsiveness of the autonomic nervous system (ANS). The other 50% repetitive values measured at the same time indicate a balanced autonomic nervous system (ANS) and good capacity to regulate it.
Alpha 1 values> 1.0 indicate better balance, good resilience, and balanced compensation and regulation processes in the body.
Alpha 1 values < 1.0 mean an increasing randomness of the measured values. This results in an imbalance in the regulatory systems.
Alpha 1 values < 0.8 indicate a high imbalance, and reduced ability to regulate/incoherence in all physical regulatory systems.
Note: The Alpha 1 value can change with increasing intensity and drop from starting values above 1.0 to values around 0.75 (at the aerobic threshold, not to be confused with the anaerobic threshold or lactate threshold!) to values around 0.5 at very high intensity.
The Alpha 2 value is often used as a non-linear parameter for longer RR intervals. According to the current state of science, reduced values are associated with a rather poor prognosis.
Note: The correct interpretation of these diagrams and values requires expert knowledge and should only be carried out by appropriately trained individuals.
SD1 and SD2 / Poincaré or Lorenz plot diagram
The Poincaré diagram is a graphical representation of the ratios of RR – interval to RR+1 interval. In other words, the first point represents the ratio of the first RR distance (X-axis) to the second subsequent RR distance (Y-axis). The next point is based on the ratio of the second RR distance (this time X-axis) to the third RR distance (Y-axis), etc.
The more different the successive RR distances are (i.e. the higher the variability), the more open and larger the displayed point cloud becomes.
SD stands for standard deviation. A standard deviation describes the fluctuation range around a mean value. With regard to heart rate variability, the SD says something about the heart rate fluctuations.
SD1
The SD1 value rather describes parasympathetic influence. It shows how quickly the heart rate changes (heart rate in milliseconds). This value is more sensitive to the fast, higher-frequency changes in heart rate. In the diagram, the SD1 describes the standard deviation of the perpendicular distances of the RRi/RRi +1 points to the transverse diameter of the ellipse -> width of the point cloud in the Poincaré (green line).
With some experience, short-term changes in heart rate variability can be read from the SD1 value. It provides information about the current condition. Reduced values indicate, for example, a present or current stress load (e.g. of an emotional nature).
SD2
The SD2 value is assigned to the sympathetic influence and reflects the slower adjustments in which the parasympathetic and sympathetic nerves are more or less active together. The SD2 value describes the standard deviation of the perpendicular distances of the RRi/RR points to the longitudinal diameter of the ellipse -> length of the point cloud in the Poincaré (blue line).
Pointcaré
The ratio of SD1 and SD2 values can be clearly seen in the Poincaré. The values SD1 and SD2 each represent the diameters that are orthogonal to each other (green and blue line). SD1 describes the parasympathetic functionality and SD2 the sympathetic functionality and its effect on the heart rate.
Derived from this, the ratio of SD1 and SD2 can be seen at a glance from the scattering of the points in the diagram or from the shape of the point cloud, and thus conclusions can be drawn about the HRV or the regulatory capacity of the ANS. Experts also see stimulus transmission disorders in the heart, unhealthy habits or emotional trauma from the Poincaré.
Example of interpretation:
If, for example, the Poincaré diagram does not show a recognizable shape (ellipse or circle) and if many points are scattered in the diagram (sometimes also nesting), the RMSSD value and the stress index must be carefully evaluated. Not infrequently, we then see RMSSD values > 300ms, which are very unlikely.
Note: Evaluations of the Poincaré diagram should only be carried out by professionally trained individuals.
Power spectrum:
The power spectrum is an important tool in the analysis of heart rate variability (HRV), which is used to visualize and quantify the distribution of the frequencies of heart rhythm components. It is determined using the Fourier transformation method from the heartbeat intervals.
The power spectrum is usually presented in the form of a diagram that shows the intensity of the heart rate components depending of their frequency.
It is divided into three main components:
Very Low Frequency (VLF), includes frequencies below 0.04 Hz, The physiological significance of the VLF component is not fully understood, but it is often associated with long-term regulation of heart rhythm. Other scientists see a dominance of sympathetic tone here.
Low Frequency (LF): This region of the power spectrum is typically between 0.04 and 0.15 Hertz (Hz). The LF component is often considered an indicator of sympathetic and parasympathetic nervous system activity, although its exact physiological significance is controversial. Increased LF activity may be associated with stress or strain. Other scientific opinions see a pronounced LF as proof of coherence, i.e. a good balance between the sympathetic and parasympathetic nervous system.
High frequency (HF): This range of the power spectrum is typically between 0.15 and 0.40 Hz. The HF component is mainly influenced by the parasympathetic activity of the autonomic nervous system and is related to breathing. The HF component normally increases during inhalation and decreases during exhalation and is often used as a measure of parasympathetic activity.
The interpretation of the ideal power spectrum in relation to heart rate variability (HRV) is complex and depends on various factors. In fact, there are different views on which components of the power spectrum should be considered ideal.
Some researchers argue that a balanced ratio of low frequency (LF) to high frequency (HF) in the power spectrum can be an indicator of healthy heart rate variability. This is often seen as a sign that the sympathetic and parasympathetic nervous systems are working together optimally and balancing each other to regulate the heart rhythm. In this sense, a higher proportion of LF activity relative to HF activity could be considered favorable.
On the other hand, some researchers argue that a dominance of the Highfrequency (HF) component in the power spectrum is an indicator of particularly pronounced parasympathetic activity and strong cardiovagal regulation. In this context, a higher proportion of HF activity in relation to LF activity is considered favorable.
The power spectrum provides insight into the autonomic regulation of cardiac rhythm and can be used to characterize various aspects of heart rate variability. However, it is important to note that the power spectrum is only a snapshot of HRV and can be influenced by various factors such as age, gender, physical activity and health status.
Overall, the ideal power spectrum of good HRV can therefore depend on various factors, and interpretation should always be made in the context of other clinical information. There is no consensus on which ratio of LF to HF in the power spectrum is best suited to characterize optimal heart rate variability.