About Heart Rate Variability (HRV)

Heart rate variability (HRV) is a physiological phenomenon where the time intervals between consecutive heartbeats vary from beat-to-beat. HRV is the most commonly used tool for objective assessment of physiological stress and recovery, but requires accurate analysis tools. Kubios HRV software provides the most detailed HRV analysis in the market and is widely used by researchers and professionals around the world.

What is heart rate variability?

Heart rate variability (HRV) is the amount by which the time interval between heartbeats (interbeat interval, IBI) varies from beat to beat. The magnitude of this variability is small (measured in milliseconds), and therefore, assessment of HRV requires specialized measurement devices and accurate analysis tools. Typically HRV is extracted from an electrocardiogram (ECG) measurement by measuring the time intervals between successive heartbeats as illustrated in the below figure.

Heart rate variability in healthy individuals is strongest during rest, whereas during stress and physical activity HRV is decreased. Please note that the magnitude of HRV is different between individuals. High HRV is commonly linked to young age, good physical fitness, and good overall health.

heart rate variability

How to measure HRV?

For accurate measurement of heart rate variability, an electrocardiogram (ECG) device may be used. ECG devices have been commonly used in the medical setting, but nowadays they are also used among consumers and non-medical professionals because several affordable Holter ECG devices (suitable for longer term measurements) as well as hand-held ECG devices (suitable for short-term intermittent measurements) are available in the market. The sampling rate of the ECG should be at least 200 Hz for accurate HRV assessment. When you choose ECG as the measurement modality for HRV assessment, you have the following advantages:

1. Accurate interbeat interval or RR interval data can be extracted from the ECG

2. Abnormal beats and possible arrhythmia episodes can be verified from the ECG waveform data

3. Robust respiratory rate (RESP) estimate can be extracted from the ECG [7], [13]. RESP is an important physiological parameter and is needed in the interpretation of frequency-domain HRV parameters.

HR monitors

In addition to ECG devices, good quality heart rate monitors which record beat-to-beat data are also suitable for HRV assessment. For example, validated HR monitors such as the Polar H10 and Movesense can be recommended. You can use these sensors with Kubios HRV mobile app to record accurate HRV data.


PPG monitors

Heart rate variability can also be measured optically, i.e. using photoplethysmogram (PPG). PPG measurement is commonly used in wrist-worn devices that measure heart rate through your skin. However, PPG monitoring is sensitive to motion artifacts and is therefore recommended only for resting measurements. You should also note that the beat detection accuracy is lower when compared to ECG signal.

Heart rate variability (HRV) measurement

Physiological origin of heart rate variability

Our body’s autonomic nervous system (ANS) regulates the heart rate (HR), vasomotor activity (causing changes in blood vessel diameter), and arterial baroreflex, mainly to stabilize blood pressure. Both sympathetic and parasympathetic branches of the ANS are involved in the regulation of the HR. Activation of the sympathetic nervous system (SNS) increases HR and decreases HRV, whereas parasympathetic nervous system (PNS) activity decreases HR and increases HRV[3]. When our body is under stress (due to stress, lack of recovery, inflammation, etc.), the sympathetic activity of the ANS is elevated and this affects the HRV. Therefore, HRV is an accurate objective measure of overall bodily stress.

Sympathetic nervous system activity and heart rate variability

How respiration affects HRV?

In a healthy individual, respiration causes notable changes in the heart rate. When we inhale (breathe in), we may observe that our heart starts beating somewhat faster while exhaling (breathing out) slows down the heartbeats. This physiological phenomenon is called respiratory sinus arrhythmia (RSA) and it is typically the most conspicuous component of the HRV. The physiological origin of the RSA is shortly the following. During inspiration, the vagus nerve stimulation is being cut-off due to decreased intrathoracic pressure, and therefore, HR increases. During expiration, intrathoracic pressure is increased activating the baroreceptors and vagus nerve stimulation, and therefore, HR decreases. For details, see the tutorial video about Respiratory Sinus Arrhythmia.

Low frequency component of HRV

The RSA component mentioned above is typically observed as a high-frequency component (HF: 0.15-0.4 Hz), assuming that the respiratory rate is within the HF band. The HF (or RSA) component of HRV is caused by parasympathetic regulation and thereby reflects PNS activity. Another conspicuous component of HRV is the low frequency component (LF: 0.04-0.15 Hz).

LF component is linked to baroreflex, where baroreceptors that are located on the walls of some large vessels can sense the stretching of vessel walls caused by pressure increase. Both sympathetic and parasympathetic activity are influenced by baroreceptor stimulation through a specific baroreflex arc. Thereby, both PNS and SNS activity have been found to affect the LF component, but the LF oscillations are considered to be dominated by the SNS. Moreover, the normalized power of the LF component may be used to assess sympathovagal balance of our nervous system.  [16][3], [9][4]

Baroreceptor reflex

Very low frequency components of HRV

The fluctuations below 0.04 Hz, on the other hand, have not been studied as much as the higher frequencies. These frequencies are commonly divided into very low frequency (VLF, 0.003-0.04 Hz) and ultra low frequency (ULF, 0-0.003 Hz) bands, but in case of short-term recordings the ULF band is generally omitted [16]. These lowest frequency rhythms are characteristic for HRV signals and have been related, for example, to humoral factors such as the thermoregulatory processes and renin-angiotensin system [3].

Preprocessing of HRV Measurements

Heart rate variability gives reliable information about the functioning of the autonomic nervous system when the measured HRV data does not contain disturbances or noise and the interbeat intervals have been identified correctly. The careful preprocessing of HRV data aims to ensure the reliability of the analysis and the correctness of the interpretations that are made from the measurement. In Kubios HRV software products, the preprocessing typically includes three steps: 1) noise detection, 2) beat correction, and 3) trend removal.

Preprocessing heart rate variability (HRV) data

Noise detection

The aim of signal quality or noise detection is to identify periods where the ECG signal is so noisy that heartbeats cannot be reliably recognized, or a period when the measured interbeat interval data is clearly corrupted. Detected noise periods can then be excluded from the analysis and thus improve the reliability of the results.


Beat correction

The aim of beat correction is to identify and correct all abnormal beat intervals in the HRV data to be analyzed [6]. Abnormal beat intervals are caused by missing beat detections, extra beat detections, or misaligned beat detections. Problems with beat detection are often caused by noise in the ECG signal, which is typical especially for ambulatory measurements. In addition, abnormalities in the heart rhythm, such ventricular ectopic beats, appear as abnormal beat intervals and must be corrected before analysis.


Trend removal

The aim of trend removal is to remove very low frequency trend components from the interbeat interval data, and thereby, make the short-term analysis of HRV more sensitive to the low and high frequency variability regulated by the sympathetic and parasympathetic branches of the autonomic nervous system [16], [3], [14]

Heart Rate Variability Analysis Methods

Heart rate variability is typically assessed by calculating various parameters that describe the magnitude or nature of the interbeat interval variability. These HRV analysis methods can be divided into time-domain, frequency-domain and nonlinear parameters.

Time-domain HRV analysis methods

Time-domain HRV analysis methods are calculated from the interbeat intervals between successive heartbeats also known as the RR intervals (time intervals between successive R-peaks of the ECG). One commonly used time-domain parameter is the root mean square of successive RR interval differences (RMSSD) which measures beat-to-beat variability and is strongly associated with the parasympathetic nervous system activity. Another commonly used time-domain measure is the standard deviation of RR intervals (SDNN) which is a measure of overall HRV, and thus, reflects both sympathetic and parasympathetic nervous activity.


Frequency-domain HRV analysis methods

Frequency-domain HRV analysis methods are calculated from the power spectrum of RR interval data and describe the distribution of heart rate variability into different frequency components. Short-term HRV recordings are typically divided into very low frequency (VLF: 0-0.04 Hz), low frequency (LF: 0.04-0.15 Hz), and high frequency (HF: 0.15-0.4 Hz) components. Powers of the frequency components are typically presented both in absolute units as well as in normalized units. When using frequency-domain analyses of HRV, one should make sure that the respiratory rate is within the HF band. In case of exercise recordings, the upper limit of the HF band should usually be raised because respiratory rate can even rise up to 1 Hz (equivalent to 60 breaths/min).


Nonlinear HRV analysis methods

Nonlinear HRV analysis methods are sometimes used to evaluate nonlinear mechanisms, complexity or chaotic behavior of heart rate variability. One commonly used nonlinear HRV analysis method is the Poincaré plot, which is a graphical representation of the correlation between RR intervals. Another quite often used nonlinear HRV parameter is the detrended fluctuation analysis (DFA), which measures the fractal behavior of HRV.

Applications of Heart Rate Variability

Analysis of heart rate variability is a commonly used approach to assess the functioning of cardiac autonomic regulation. Both sympathetic and parasympathetic nervous systems take place in regulation of heart rate, and therefore, HRV is an indirect tool to evaluate the functioning and balance of the autonomic nervous system (ANS). This makes HRV a powerful tool for different health and wellbeing as well as sports and exercise applications.

One of the main clinical scenarios where heart rate variability has been found valuable include the risk stratification of sudden cardiac death after acute myocardial infarction [16][1][5][10]. In addition, decreased HRV is generally accepted to provide an early warning sign of diabetic cardiovascular autonomic neuropathy [16][1], the most significant decrease in HRV being found within the first 5-10 years of diabetes [18][15].

Besides these two main clinical scenarios, HRV has been studied with relation to several cardiovascular diseases, renal failure, physical exercise, occupational and psychosocial stress, gender, age, drugs, alcohol, smoking, sleep, etc. [17][8][16][11][2][1]

Frequently Asked Question (FAQ)

What is a healthy heart rate variability?

Every individual is different and has a variability that’s “normal” for them. Though it varies with age, Studies show that in healthy adults, the heart variability rate (RMSSD) should be between 19 and 75 milliseconds.


Should I worry if my HRV is low?

In general, low heart rate variability is considered a sign of current or future health problems because it reflects that your body is less resilient and has trouble handling changing situations. So, yes you should pay some attention and consult a specialist if you have chronically low HRV.


How do I increase my heart rate variability?

A healthy lifestyle is the key to higher HRV result. You should pay attention, for example, to eating healthy, getting enough sleep, reducing stress, regular exercise, etc.


Why is my HRV low at night?

In general, heart rate variability is high during nighttime. Higher HRV has been linked to deeper sleep stages, while during REM sleep increased sympathetic activity may lower HRV. If variability of heart rate is abnormally low at night, it is an indication of poor sleep quality and possible sleep disorder.


Why is my HRV always so low?

Low HRV indicates that your sympathetic nervous system (fight-or-flight response) is dominant, which may be due to accumulated stress, poor sleep quality, lack of recovery, or otherwise unhealthy lifestyle.


    1. 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.
    2. J. Achten and A.E. Jeukendrup. Heart rate monitoring – applications and limitations. Sports Med, 33(7):517–538, 2003.
    3. 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.
    4. R. Furlan, A. Porta, F. Costa, J. Tank, L. Baker, R. Schiavi, D. Robertson, A. Malliani, and R. Mosqueda-Garcia. Oscillatory patterns in sympathetic neural discharge and cardiovascular variables during orthostatic stimulus. Circulation, 101:886–892, 2000.
    5. T. Laitio, J. Jalonen, T. Kuusela, and H. Scheinin. The role of heart rate variability in risk stratification for adverse postoperative cardiac events. Anesth Analg, 105(6):1548–1560, 2007.
    6. J.A. Lipponen and M.P. Tarvainen. A robust algorithm for heart rate variability time series artefact correction using novel beat classification. J Med Eng Technol, 43(3):173-181, 2019.
    7. Lipponen JA and Tarvainen MP. Accuracy of Kubios HRV software respiratory rate estimation algorithms. Kubios white paper, 2021.
    8. M. Malik and A.J. Camm. Components of heart rate variability – what they really mean and what we really measure. Am J Cardiol, 72(11):821–822, 1993.
    9. M. Pagani, N. Montano, A. Porta, A. Malliani, , F.M. Abboud, C. Birkett, and V.K. Somers. Relationship between spectral components of cardiovascular variabilities and direct measures of muscle sympathetic nerve activity in humans. Circulation, 95:1441–1448, 1997.
    10. O. Pradhapan, M.P. Tarvainen, T. Nieminen, R. Lehtinen, K. Nikus, T. Lehtimäki, M. Kähönen, and J. Viik. Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortality – an experimental study on the FINCAVAS cohort. Frontiers in Physiology, 5(Article 208):1–9, 2014.
    11. J. Pumprla, K. Howorka, D. Groves, M. Chester, and J. Nolan. Functional assessment of heart rate variability: physiological basis and practical applications. Int J Cardiol, 84:1–14, 2002.
    12. J.A. Richman and J.R. Moorman. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol, 278:H2039–H2049, 2000.
    13. Rogers B, Schaffarczyk M, and Gronwald T. Estimation of respiratory frequency in women and men by Kubios HRV software using the Polar H10 and Movesense Medical ECG sensor during an exercise ramp, Sensors, 22(Article 7156):1-12, 2022a.
    14. M.P. Tarvainen, P.O. Ranta-aho, and P.A. Karjalainen. An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng, 49(2):172-175, 2002.
    15. M.P. Tarvainen, T.P. Laitinen, J.A. Lipponen, D.J. Cornforth, and H.F. Jelinek. Cardiac autonomic dysfunction in type 2 diabetes – effect of hyperglycemia and disease duration. Frontiers in Endocrinology, 5(Article 130):1–9, 2014.
    16. 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.
    17. C.M.A. van Ravenswaaij-Arts, L.A.A. Kollée, J.C.W. Hopman, G.B.A. Stoelinga, and H.P. van Geijn. Heart rate variability. Ann Intern Med, 118(6):436–447, 1993.
    18. A.I. Vinik, T. Erbas, and C.M. Casellini. Diabetic cardiac autonomic neuropathy, inflammation and cardiovascular disease. J Diabetes Investig, 4(1):4–8, 2013.