Heart rate variability (HRV) reflects variations between consecutive inter-beat-intervals (RR intervals). Both sympathetic and parasympathetic branches of the autonomic nervous system (ANS) are involved in the regulation of heart rate (HR). Parasympathetic nervous system (PNS) activity (vagal stimulation) is known to decrease heart rate and increase heart rate variability. The sympathetic nervous system (SNS) activity has more or less the opposite effect on heart rate and heart rate variability, i.e. it increases HR and decreases HRV. Therefore, HR is lowest and HRV is highest when we are in rest and fully recovered. During stressful situations when sympathetic nervous activity is increased, resting heart rate is elevated and and heart rate variability is decreased.

#### Parasympathetic nervous system (PNS) index

Parasympathetic cardiac activity is known to 1) decrease heart rate (i.e. increase the time interval between successive heart beats), 2) increase HRV via enhanced respiratory sinus arrhythmia (RSA) component (i.e. increasing the quick changes in RR interval linked to respiration – shortening of RR intervals during inhalation and lengthening of RR intervals during exhalation), and 3) decrease the ratio between lower frequency and higher frequency oscillations in HRV time series (i.e. increase the relative amount of quick RSA originated fluctuations in HRV compared to slower short-term fluctuations) ^{Task Force 1996, Berntson et al. 1997, Acharya et al. 2006}.

Based on the above, the parasympathetic nervous system index (PNS index) is computed in Kubios HRV software based on the following three parameters:

**Mean RR**interval. Longer mean RR interval means lower heart rate and higher parasympathetic cardiac activation.**Root mean square of successive RR interval differences (RMSSD)**, which is a commonly used time-domain HRV parameter that captures the quick beat-to-beat changes in RR interval, and therefore, strongly linked to RSA component magnitude. High values of RMSSD indicate strong RSA component and high parasympathetic cardiac activation.**Poincaré plot index SD1 in normalized units**. A commonly used approach for estimating the sympathovagal balance of the ANS is to compute the low frequency (LF) to high frequency (HF) power ration from HRV spectrum. However, in case of spontaneous breathing, especially when the natural breathing rate of the subject is low (below 0.15 Hz or 9 breaths/min), the RSA component is partially or even completely overlapping with the LF component. In such cases the LF/HF ratio gives invalid interpretation of ANS status. Since Poincare plot index SD1 is known to be linked to RMSSD^{Brennan et al. 2001}and the ratio SD2/SD1 correlates with LF/HF ratio, the normalized SD1 value is used in Kubios HRV as the third input parameter for the PNS index computation.

Each parameter value is first compared to their normal population values as presented in ^{Nunan et al. 2010}. The normal value for the SD1 is derived based on its dependency on the time-domain variable RMSSD as described in ^{Brennan et al. 2001}. The parameter values are then scaled with the standard deviations of normal population and finally a proprietary weighting is applied to obtain robust and reliable PNS index value.

The interpretation of the PNS index is straightforward. A PNS index value of zero means that the three parameters reflecting parasympathetic activity are on average equal to the normal population average. Correspondingly, a positive PNS index value tells how many SDs above the normal population average the parameter values are, whereas a negative value tells how many SDs below the normal population average the parameter values are. Please note that the normal values presented in Nunan et al. 2010 are extracted from resting HRV measurements. Thus in rest, the PNS index is typically (with 95% of population) between -2 and +2, i.e. within ±2SD of the normal population distribution (see Fig. 1). During stress or during high intensity exercise much lower PNS index values can be expected.

**Figure 1:** Parasympathetic nervous system (PNS) index.

**Sympathetic nervous system (SNS) index**

Sympathetic cardiac activity is known to 1) increase heart rate, 2) decrease HRV, reducing especially quick RSA related changes in RR interval, and 3) increase the ratio between lower frequency and higher frequency oscillations in HRV data^{Task Force 1996, Berntson et al. 1997, Acharya et al. 2006}.

Based on the above, the sympathetic nervous system index (SNS index) is computed in Kubios HRV software based on the following three parameters:

**Mean HR**interval. Higher heart rate is linked to higher sympathetic cardiac activation.**Baevsky’s stress index (SI)**, which is a geometric measure of HRV reflecting cardiovascular system stress. High values of SI indicate reduced variability and high sympathetic cardiac activation.**Poincaré plot index SD2 in normalized units**. As mentioned above (in PNS index description) LF/HF power ratio is commonly used assessing sympathovagal balance of the ANS, which however is sensitive to breathing rate. Thus normalized Poincare plot index SD2, which is known to be linked to SDNN^{Brennan et al. 2001}and to correlate with LF/HF ratio, is used in Kubios HRV as the third input parameter for the SNS index computation.

Each parameter value is first compared to their normal population values as presented in ^{Nunan et al. 2010}. The normal value for the SD2 is derived based on its dependency on the time-domain variable SDNN as described in ^{Brennan et al. 2001}. The normal values for the Baevsky’s stress index are taken from ^{Baevsky 2009}. The parameter values are then scaled with the standard deviations of normal population and finally a proprietary weighting (taking into account associations between exercise intensity, heart rate and heart rate variability) is applied to obtain the SNS index value (see Fig. 2).

The interpretation of the SNS index is similar to PNS index. A SNS index value of zero means that the three parameters reflecting sympathetic activity are on average equal to the normal population average. Correspondingly, a positive SNS index value tells how many SDs above the normal population average the parameter values are, a negative value tells how many SDs below the normal population average the parameter values are. During stress or high intensity exercise SNS index can have values as high as 5-35.

**Figure 2:** Sympathetic nervous system (SNS) index.

#### Assessing stress and recovery using Kubios HRV analysis

By using time-varying analysis (available in Kubios HRV Premium), time trends for different HRV analysis parameters can be obtained. A good choice for the analysis window is 5-10 minutes ^{Task Force 1996}, which provides sufficient time resolution for analyzing long-term (24-hours or more) measurements and enables accurate estimation of HRV parameters. For shorter term measurements, the analysis window can be shortened to increase the time resolution, i.e. to capture smaller details in HRV dynamics. Note that the accuracy of some HRV parameters (especially the nonlinear parameters) decreases when shortening the analysis window.

An example of time-varying analysis for assessing stress and recovery using Kubios HRV analysis is presented in Fig. 3. The figure illustrates time-varying HRV analysis results for a 48-hour HRV recording from a healthy young male subject. The results (taken from Kubios HRV Premium report) show heart rate, PNS index and SNS index graphs and their statistics. Recovery during sleep can be observed as high levels of PNS index.

**Figure 3:** An example of stress and recovery monitoring. The figure shows time-varying analysis of 48-hour HRV recording (using Kubios HRV Premium software) for a young healthy male subject.

#### References

- U.R. Acharya, K.P. Joseph, N. Kannathal, C.M. Lim, and J.S. Suri. Heart rate variability: a review. Med Biol Eng Comput, 44:1031–1051, 2006.
- R. M. Baevsky. Methodical recommendations use kardivar system for determination of the stress level and estimation of the body adaptability standards of measurements and physiological interpretation. 2009.
- G.G. Berntson, J.T. Bigger Jr., D.L. Eckberg, P. Grossman, P.G. Kaufmann, M. Malik, H.N. Nagaraja, S.W. Porges, J.P. Saul, P.H. Stone, and M.W. Van Der Molen. Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiol, 34:623–648, 1997.
- M. Brennan, M. Palaniswami, and P. Kamen. Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability. IEEE Trans Biomed Eng, 48(11):1342–1347, 2001.
- D. Nunan, G.R.H. Sandercock, and D.A. Brodie. A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. PACE, 33:1407–1417, November 2010.
- Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart rate variability – standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5):1043–1065, March 1996.