Heart rate variability (HRV) reflects the variations between consecutive inter-beat intervals, known as RR intervals or IBIs. Both the sympathetic (SNS) and parasympathetic (PNS) branches of the autonomic nervous system (ANS) regulate heart rate (HR). While PNS activity typically decreases HR and increases HRV, SNS activity tends to increase HR and decrease HRV. Thus, HR is lowest and HRV is highest during rest and recovery when the parasympathetic or vagal activation is highest. Conversely, during stress, when SNS activity is elevated, HR increases and HRV decreases.
Parasympathetic nervous system (PNS) index
The PNS index measures parasympathetic cardiac activity, which influences heart rate variability (HRV) by decreasing heart rate, enhancing HRV via increased respiratory sinus arrhythmia (RSA), and reducing the ratio between lower and higher frequency oscillations in HRV time series [1], [3], [6]. The PNS index in Kubios HRV software is calculated using three parameters:
- Mean RR Interval: A longer mean RR interval indicates a lower heart rate and higher parasympathetic activity.
- Root Mean Square of Successive Differences (RMSSD): This time-domain HRV parameter captures quick beat-to-beat changes in the RR interval, strongly linked to RSA magnitude. Higher RMSSD values indicate strong RSA component and high parasympathetic activity.
- Poincaré Plot Index SD1 in Normalized Units. Commonly, the low frequency (LF) to high frequency (HF) power ratio from the HRV spectrum is used to estimate the sympathovagal balance. However, in cases of spontaneous breathing at rates below 0.15 Hz, the RSA component overlaps with the LF component, making the LF/HF ratio potentially misleading. The SD1 index, linked to RMSSD [4], and the SD2/SD1 ratio, correlating with the LF/HF ratio, are used as more reliable metrics in such scenarios.
Each parameter’s value is first standardized against normal population values as presented in [5], with the normal value for SD1 informed by its relationship with RMSSD as detailed in [4]. These values are then scaled by the standard deviations of the normal population, and a proprietary weighting is applied to derive a robust PNS index.
Interpreting the PNS Index:
A PNS index value of zero indicates that the parameters reflecting parasympathetic activity are, on average, equivalent to those of the normal population. Positive index values signify levels above the norm, and negative values indicate levels below. Typically, during rest, the PNS index ranges within ±2 standard deviations of the normal population distribution. During stress or high-intensity exercise, lower PNS index values are expected, reflecting decreased parasympathetic activity.
Figure 1: Parasympathetic nervous system (PNS) index.
Sympathetic nervous system (SNS) index
The SNS index evaluates sympathetic cardiac activity, which typically increases heart rate, decreases HRV by reducing rapid RSA-related changes, and raises the ratio between lower and higher frequency oscillations in HRV data [1], [3], [6]. In Kubios HRV software, the SNS index is calculated from three key parameters:
- Mean HR: A higher heart rate suggests increased sympathetic activation.
- Baevsky’s Stress Index (SI): This geometric HRV measure reflects cardiovascular stress, with high values indicating significant sympathetic activation and reduced variability.
- Poincaré Plot Index SD2 in Normalized Units: This index correlates with SDNN as discussed in [4], as well as the LF/HF ratio, providing a nuanced view of the sympathovagal balance that adjusts for variable breathing rates.
Each parameter is standardized against normal population values, with references from [5] and [2], and scaled by the standard deviations of these norms. A proprietary weighting process, considering the relationships between exercise intensity, heart rate, and HRV, is applied to compute the SNS index.
Interpreting the SNS Index:
A SNS index of zero indicates average sympathetic activity compared to the norm. Positive values reflect sympathetic activity levels above the norm, while negative values indicate lower than average activity. During stress or intense exercise, the SNS index can range significantly higher, potentially reaching values between 5 and 35.
Figure 2: Sympathetic nervous system (SNS) index.
PNS vs. SNS index
PNS (Parasympathetic Nervous System) and SNS (Sympathetic Nervous System) indexes provide distinct insights into autonomic function by quantifying the influence of the parasympathetic and sympathetic branches, respectively. While the PNS index reflects recovery and relaxation states, indicating higher HRV and lower heart rate, the SNS index reveals stress responses, characterized by lower HRV and elevated heart rate. These indexes are crucial for assessing balance in autonomic nervous system activity, enhancing the understanding of physiological responses to stress and relaxation.
Assessing stress and recovery using Kubios HRV analysis software
Kubios HRV Scientific’s time-varying analysis enables the observation of trends in various HRV parameters over time. A 5-10 minute analysis window is recommended for long-term measurements (24 hours or more) to ensure detailed and accurate HRV parameter estimation [6]. For shorter measurements, reducing the window can enhance time resolution, capturing finer details in HRV dynamics, though this may affect the accuracy of certain parameters, especially nonlinear ones. An example of time-varying analysis in Kubios HRV Scientific is shown in Fig. 3, displaying a 48-hour HRV recording from a healthy young male. The results include heart rate, PNS index, and SNS index graphs, highlighting recovery periods during sleep, as evidenced by elevated PNS index levels.
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.
Frequently Asked Question (FAQ)
What are the PNS and SNS indexes and how do they differ?
The PNS (Parasympathetic Nervous System) and SNS (Sympathetic Nervous System) indexes measure the activity of the respective branches of the autonomic nervous system. The PNS index reflects relaxation and recovery states, typically associated with lower heart rates and higher HRV. Conversely, the SNS index indicates stress response, characterized by higher heart rates and lower HRV.
How are the PNS and SNS indexes calculated in Kubios HRV software?
In Kubios HRV software, the PNS index is calculated using metrics such as the mean RR interval, RMSSD, and normalized Poincaré plot index SD1, which emphasize parasympathetic activity. The SNS index utilizes the mean HR, Baevsky’s Stress Index, and normalized Poincaré plot index SD2, focusing on sympathetic influences. Both indexes adjust for baseline values and apply proprietary weighting to provide a robust measure of autonomic balance.
Can the PNS and SNS indexes help in managing lifestyle and stress?
Yes, the PNS and SNS indexes can be instrumental in lifestyle and stress management by providing insights into your autonomic nervous system’s balance. Monitoring these indexes can help you understand how different activities, stress levels, and relaxation techniques affect your body, aiding in making informed decisions to improve stress management and overall well-being.
What can the PNS and SNS indexes reveal about my health?
The PNS and SNS indexes can reveal important aspects of your autonomic health, such as your body’s ability to manage stress and recover. High PNS and low SNS readings at rest typically suggest better stress management and recovery capabilities. Conversely, elevated SNS levels at rest may indicate chronic stress, potentially leading to health issues.
How do changes in the PNS and SNS indexes correlate with physical activity or stress?
Changes in the PNS and SNS indexes directly correlate with physical activity and stress levels. During physical activity or stress, the SNS index tends to increase, reflecting heightened sympathetic activity. Recovery periods or relaxation practices enhance PNS activity, reflected by a higher PNS index, indicating effective stress management and recuperation.
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.
