Heart rate variability

Variability (or Variation) in the beat-to-beat interval is a physiological phenomenon. Power spectral analysis of the beat-to-beat variations of heart rate or the heart period (R–R interval) partitions the total variance (the “power”) of a continuous series of beats into its frequency components, typically identifying two or three main peaks: Very Low Frequency (VLF) <0.04 Hz, Low Frequency (LF), 0.04–0.15 Hz, and High Frequency (HF) 0.15–0.4 Hz.{{cite journal |vauthors=Billman GE |date=2013 |title=The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance |journal=Frontiers in Physiology |volume=4 |pages=26 |doi=10.3389/fphys.2013.00026 |pmc=3576706 |pmid=23431279 |doi-access=free}}

The HF peak is widely believed to reflect cardiac parasympathetic nerve (PSNS) activity while the LF, although more complex, is often assumed to have a dominant sympathetic (SNS) component. Though LF was previously thought to reflect primarily SNS activity, it is now widely accepted that it reflects a mixture of both the SNS and PSNS.

Decreased PSNS activity or increased SNS activity will result in reduced HRV. High frequency (HF) activity (0.15 to 0.40 Hz), especially, has been linked to PSNS activity. Activity in this range is associated with the respiratory sinus arrhythmia (RSA), a vagally mediated modulation of heart rate (which increases during inspiration and decreases during expiration). Less is known about the physiological inputs of the low frequency (LF) activity (0.04 to 0.15 Hz).

The SA node receives several different inputs and the instantaneous heart rate or RR interval and its variation are the results of such inputs.{{cite journal |vauthors=Cooper HE, Clutton-Brock TM, Parkes MJ |year=2004 |title=Contribution of the respiratory rhythm to sinus arrhythmia in normal unanesthetized subjects during positive-pressure mechanical hyperventilation |url=https://www.physiology.org/doi/10.1152/ajpheart.00504.2003 |journal=American Journal of Physiology |volume=288 |issue=1 |pages=H402-411 |doi=10.1152/ajpheart.00504.2003 |pmid=12958033|url-access=subscription }} The main inputs are the sympathetic and the parasympathetic nervous system (PSNS) and humoral factors. Respiration gives rise to waves in heart rate mediated primarily via the PSNS, and it is thought that the lag in the baroreceptor feedback loop may give rise to 10 second waves in heart rate (associated with Mayer waves of blood pressure), but this remains controversial.{{citation needed|date=March 2021}}

Factors that affect the input are the baroreflex, thermoregulation, hormones, sleep–wake cycle, meals, physical activity, and stress.

Reduced HRV has been shown to be a predictor of mortality after myocardial infarction{{cite journal |vauthors=Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN |date=January 1992 |title=Frequency domain measures of heart period variability and mortality after myocardial infarction |journal=Circulation |volume=85 |issue=1 |pages=164–171 |doi=10.1161/01.CIR.85.1.164 |pmid=1728446 |doi-access=free}}{{cite journal |vauthors=Kleiger RE, Miller JP, Bigger JT, Moss AJ |date=February 1987 |title=Decreased heart rate variability and its association with increased mortality after acute myocardial infarction |journal=The American Journal of Cardiology |volume=59 |issue=4 |pages=256–262 |doi=10.1016/0002-9149(87)90795-8 |pmid=3812275 |doi-access=free}} although others have shown that the information in HRV relevant to acute myocardial infarction survival is fully contained in the mean heart rate.{{cite journal |vauthors=de Geus EJ, Gianaros PJ, Brindle RC, Jennings JR, Berntson GG |date=February 2019 |title=Should heart rate variability be "corrected" for heart rate? Biological, quantitative, and interpretive considerations |journal=Psychophysiology |volume=56 |issue=2 |pages=e13287 |doi=10.1111/psyp.13287 |pmc=6378407 |pmid=30357862}}

A range of other outcomes and conditions may also be associated with modified (usually lower) HRV, including congestive heart failure, diabetic neuropathy, post–cardiac-transplant depression, susceptibility to SIDS and poor survival in premature babies,{{citation needed|date=March 2021}} as well as fatigue severity in chronic fatigue syndrome.{{cite journal |vauthors=Escorihuela RM, Capdevila L, Castro JR, Zaragozà MC, Maurel S, Alegre J, Castro-Marrero J |date=January 2020 |title=Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis |journal=Journal of Translational Medicine |volume=18 |issue=1 |pages=4 |doi=10.1186/s12967-019-02184-z |pmc=6943898 |pmid=31906988 |doi-access=free}} On the other hand, for patients having high blood pressure (hypertension), higher HRV is a risk factor for atrial fibrillation.{{cite journal |vauthors=Kim SH, Lim KR, Chun KJ |year=2022 |title=Higher heart rate variability as a predictor of atrial fibrillation in patients with hypertensione |journal=Scientific Reports |volume=12 |issue=1 |pages=3702 |bibcode=2022NatSR..12.3702K |doi=10.1038/s41598-022-07783-3 |pmc=8904557 |pmid=35260686}}

File:Neurovisceral integration model.png

There is interest in HRV in the field of psychophysiology. For example, HRV is related to emotional arousal. High-frequency (HF) activity has been found to decrease under conditions of acute time pressure and emotional strain{{cite journal | vauthors = Nickel P, Nachreiner F | title = Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload | journal = Human Factors | volume = 45 | issue = 4 | pages = 575–590 | year = 2003 | pmid = 15055455 | doi = 10.1518/hfes.45.4.575.27094 | s2cid = 27744056 }} and elevated anxiety state,{{cite journal | vauthors = Jönsson P | title = Respiratory sinus arrhythmia as a function of state anxiety in healthy individuals | journal = International Journal of Psychophysiology | volume = 63 | issue = 1 | pages = 48–54 | date = January 2007 | pmid = 16989914 | doi = 10.1016/j.ijpsycho.2006.08.002 }} presumably related to focused attention and motor inhibition. HRV has been shown to be reduced in individuals reporting to worry more.{{cite journal | vauthors = Brosschot JF, Van Dijk E, Thayer JF | title = Daily worry is related to low heart rate variability during waking and the subsequent nocturnal sleep period | journal = International Journal of Psychophysiology | volume = 63 | issue = 1 | pages = 39–47 | date = January 2007 | pmid = 17020787 | doi = 10.1016/j.ijpsycho.2006.07.016 }} In individuals with post-traumatic stress disorder (PTSD), HRV and its HF component is reduced whilst the low-frequency (LF) component is elevated. Furthermore, PTSD patients demonstrated no LF or HF reactivity to recalling a traumatic event.{{cite journal | vauthors = Cohen H, Kotler M, Matar MA, Kaplan Z, Loewenthal U, Miodownik H, Cassuto Y | title = Analysis of heart rate variability in posttraumatic stress disorder patients in response to a trauma-related reminder | journal = Biological Psychiatry | volume = 44 | issue = 10 | pages = 1054–1059 | date = November 1998 | pmid = 9821570 | doi = 10.1016/S0006-3223(97)00475-7 | s2cid = 36273872 }} Statistical quantitative differences have also been found among healthy, depressed, and psychotic people. {{cite journal | vauthors = Sabelli H, Messer J, Kovacevic L, Walthall K | title = Biotic patterns of heart rate variation in depressed and psychotic subjects. | journal = Nonlinear Dynamics, Psychology, and Life Sciences | volume = 15 | issue = 1 | pages = 11–28 | date = January 2011 | pmid = 21176437 }}

The neurovisceral integration is a model of HRV that views the central autonomic network as the decision maker of cognitive, behavioral and physiological regulation as they pertain to a continuum of emotion.{{Cite journal |vauthors=Appelhans BM, Luecken LJ |date=September 2006 |title=Heart Rate Variability as an Index of Regulated Emotional Responding |journal=Review of General Psychology |volume=10 |issue=3 |pages=229–240 |doi=10.1037/1089-2680.10.3.229 |issn=1089-2680 |s2cid=3926266}} The neurovisceral integration model describes how the prefrontal cortex regulates activity in limbic structures which act to suppress parasympathetic nervous system (PSNS) activity and activate sympathetic nervous system (SNS) circuits.{{cite journal | vauthors = Thayer JF, Sternberg E | title = Beyond heart rate variability: vagal regulation of allostatic systems | journal = Annals of the New York Academy of Sciences | volume = 1088 | issue = 1 | pages = 361–372 | date = November 2006 | pmid = 17192580 | doi = 10.1196/annals.1366.014 | bibcode = 2006NYASA1088..361T | s2cid = 30269127 }} Variation in the output of these two branches of the autonomic system produces HRV{{cite journal |vauthors=Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ |title=Heart rate variability. Standards of measurement, physiological interpretation, and clinical use |journal=European Heart Journal |volume=17 |issue=3 |pages=354–381 |date=March 1996 |pmid=8737210 |doi=10.1093/oxfordjournals.eurheartj.a014868 |doi-access=free}} and activity in the prefrontal cortex can hence modulate HRV.{{cite journal | vauthors = Napadow V, Dhond R, Conti G, Makris N, Brown EN, Barbieri R | title = Brain correlates of autonomic modulation: combining heart rate variability with fMRI | journal = NeuroImage | volume = 42 | issue = 1 | pages = 169–177 | date = August 2008 | pmid = 18524629 | pmc = 2603289 | doi = 10.1016/j.neuroimage.2008.04.238 }}

HRV is reported to be an index of the influence of both the parasympathetic and the sympathetic nervous system.{{cite journal | vauthors = Thayer JF, Lane RD | title = Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration | journal = Neuroscience and Biobehavioral Reviews | volume = 33 | issue = 2 | pages = 81–88 | date = February 2009 | pmid = 18771686 | doi = 10.1016/j.neubiorev.2008.08.004 | s2cid = 15881998 }} For example, high HRV is shown to reflect proper emotion regulation, decision-making, and attention, and low HRV reflects the opposite. The parasympathetic nervous system works to decrease the heart rate, while the SNS works to increase the heart rate. For example, someone with high HRV may reflect increased parasympathetic activity, and someone with low HRV may reflect increased sympathetic activity.{{cite journal | vauthors = Laborde S, Mosley E, Thayer JF | title = Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting | journal = Frontiers in Psychology | volume = 8 | pages = 213 | date = 2017-02-20 | pmid = 28265249 | pmc = 5316555 | doi = 10.3389/fpsyg.2017.00213 | doi-access = free }}

Emotions stem from the time and impact of a situation on a person.{{cite journal | vauthors = Thayer JF, Lane RD | title = A model of neurovisceral integration in emotion regulation and dysregulation | journal = Journal of Affective Disorders | volume = 61 | issue = 3 | pages = 201–216 | date = December 2000 | pmid = 11163422 | doi = 10.1016/s0165-0327(00)00338-4 | url = https://zenodo.org/record/1259859 }} The ability to regulate emotions is essential for social environments and well-being. HRV has provided a window to the physiological components associated with emotional regulation. HRV has been shown to reflect emotional regulation at two different levels, while resting and while completing a task. Research suggests that a person with higher HRV while resting can provide more appropriate emotional responses compared to those that have low HRV at rest. Empirical research found that HRV can reflect better emotional regulation by those with higher resting HRV, particularly with negative emotions.{{cite journal | vauthors = Choi KH, Kim J, Kwon OS, Kim MJ, Ryu YH, Park JE | title = Is heart rate variability (HRV) an adequate tool for evaluating human emotions? - A focus on the use of the International Affective Picture System (IAPS) | journal = Psychiatry Research | volume = 251 | pages = 192–196 | date = May 2017 | pmid = 28213189 | doi = 10.1016/j.psychres.2017.02.025 | doi-access = free }} However, HRV is elevated by negative news in persons who react more strongly to negative news than to positive news.{{cite journal | vauthors = Soroka S, Fournier P, Nir L | title=Cross-national evidence of a negativity bias in psychophysiological reactions to news | journal= Proceedings of the National Academy of Sciences of the United States of America | volume=116 | issue=38 | pages=18888–18892 | year=2019 | doi= 10.1073/pnas.1908369116 | doi-access=free | pmc=6754543 | pmid=31481621| bibcode=2019PNAS..11618888S }} When completing a task, HRV is subject to change, especially when people need to regulate their emotions. Most importantly, individual differences are related to the ability to regulate emotions.{{cite journal | vauthors = Park G, Thayer JF | title = From the heart to the mind: cardiac vagal tone modulates top-down and bottom-up visual perception and attention to emotional stimuli | journal = Frontiers in Psychology | volume = 5 | pages = 278 | date = 2014-05-01 | pmid = 24817853 | pmc = 4013470 | doi = 10.3389/fpsyg.2014.00278 | doi-access = free }}

Previous research has suggested that a large part of the attention regulation is due to the default inhibitory properties of the prefrontal cortex. Top-down processes from the prefrontal cortex provide parasympathetic influences, and if for some reason, those influences are active, attention can suffer. Researchers have suggested that HRV can index attention. It was found that groups with high anxiety and low HRV have poor attention.{{cite journal | vauthors = Ramírez E, Ortega AR, Reyes Del Paso GA | title = Anxiety, attention, and decision making: The moderating role of heart rate variability | journal = International Journal of Psychophysiology | volume = 98 | issue = 3 Pt 1 | pages = 490–496 | date = December 2015 | pmid = 26555079 | doi = 10.1016/j.ijpsycho.2015.10.007 }} In line with this research, it has also been suggested that increased attention has been linked to high HRV and increased vagus nerve activity. The vagus nerve activity reflects the physiological modulation of the parasympathetic and sympathetic nervous system. The activity behind the prefrontal cortex and the parasympathetic and sympathetic nervous system can influence heart activity. However, people are not all affected the same. A systematic review of HRV and cognitive function suggested that resting HRV can predict individual differences in attentional performance. Furthermore, HRV has been able to index the role of attention and performance, supporting high HRV as a biomarker of increased attention and performance.{{cite journal | vauthors = Colzato LS, Steenbergen L | title = High vagally mediated resting-state heart rate variability is associated with superior action cascading | journal = Neuropsychologia | volume = 106 | pages = 1–6 | date = November 2017 | pmid = 28866318 | doi = 10.1016/j.neuropsychologia.2017.08.030 | s2cid = 7709564 | oclc = 1051786844 }}

Decision-making skills are found to be indexed by HRV in several studies. Previous research has suggested that both emotion and attention are linked to decision making; for example, poor decision making is linked to the inability to regulate or control emotions and attention and vice versa. Decision making is negatively affected by lower HRV and positively affected by higher levels of HRV. Most importantly, resting-state HRV was found to be a significant predictor of cognitive functions such as decision making.{{cite journal |vauthors=Forte G, Favieri F, Casagrande M |date=2019-07-09 |title=Heart Rate Variability and Cognitive Function: A Systematic Review |journal=Frontiers in Neuroscience |volume=13 |pages=710 |doi=10.3389/fnins.2019.00710 |pmc=6637318 |pmid=31354419 |doi-access=free}} HRV, accompanied by a psychological state such as anxiety, has been found to lead to poor decisions. For example, a group of researchers found that low HRV was an index of higher uncertainty leading to poor decision-making skills, especially those with higher levels of anxiety. HRV was also used to assess decision-making skills in a high-risk game and was found to be an index higher sympathetic activation (lower HRV) when making decisions involving risk.{{cite journal | vauthors = Shapiro MS, Rylant R, de Lima A, Vidaurri A, van de Werfhorst H | title = Playing a rigged game: Inequality's effect on physiological stress responses | journal = Physiology & Behavior | volume = 180 | pages = 60–69 | date = October 2017 | pmid = 28818539 | doi = 10.1016/j.physbeh.2017.08.006 | s2cid = 23760016 }} HRV can index psychological concepts, such as the ones outlined above, to assess the demand for the situations that people experience.

The polyvagal theory{{cite journal | vauthors = Porges SW | title = The Polyvagal Theory: phylogenetic contributions to social behavior | journal = Physiology & Behavior | volume = 79 | issue = 3 | pages = 503–513 | date = August 2003 | pmid = 12954445 | doi = 10.1016/S0031-9384(03)00156-2 | s2cid = 14074575 }}{{cite book| vauthors = Porges SW |title=The polyvagal theory : neurophysiological foundations of emotions, attachment, communication, and self-regulation|publisher=W. W. Norton|location=New York|isbn=978-0393707007|edition=1st|year=2011}} is another way to describe the pathways in the autonomic nervous system that mediate HRV. The polyvagal theory highlights three main ordinal processes, inactive response to an environmental threat, the active response to an environmental threat, and the fluctuation between the connect and disconnect to an environmental threat. This theory, like others,{{cite journal | vauthors = Thayer JF |title=Heart Rate Variability: A Neurovisceral Integration Model |journal=Encyclopedia of Neuroscience |date=2009 |pages=1041–1047 |doi=10.1016/B978-008045046-9.01991-4 |isbn=9780080450469 }} decomposes heart rate variability based on frequency domain characteristics. However, it places more emphasis on respiratory sinus arrhythmia and its transmission by a hypothesized neural pathway distinct from other components of HRV.{{cite journal | vauthors = Porges SW | title = The polyvagal perspective | journal = Biological Psychology | volume = 74 | issue = 2 | pages = 116–143 | date = February 2007 | pmid = 17049418 | pmc = 1868418 | doi = 10.1016/j.biopsycho.2006.06.009 }} There is anatomic{{cite journal | vauthors = Haselton JR, Solomon IC, Motekaitis AM, Kaufman MP | title = Bronchomotor vagal preganglionic cell bodies in the dog: an anatomic and functional study | journal = Journal of Applied Physiology | volume = 73 | issue = 3 | pages = 1122–1129 | date = September 1992 | pmid = 1400025 | doi = 10.1152/jappl.1992.73.3.1122 }} and physiological{{cite journal | vauthors = Gatti PJ, Johnson TA, Massari VJ | title = Can neurons in the nucleus ambiguus selectively regulate cardiac rate and atrio-ventricular conduction? | journal = Journal of the Autonomic Nervous System | volume = 57 | issue = 1–2 | pages = 123–127 | date = February 1996 | pmid = 8867095 | doi = 10.1016/0165-1838(95)00104-2 }} evidence for a polyvagal control of the heart.

There are two primary fluctuations:

• Respiratory arrhythmia (or respiratory sinus arrhythmia){{cite book | vauthors = Hales S | title = Statistical Essays: Containing Haemastaticks. | location = London | publisher = Innys, Manby and Woodward | year = 1733 }}

von Haller A. Elementa Physiologica. Lausanne, Switzerland: 1760; T II, Lit VI, 330 is directly caused by the central respiratory rhythm, which tracks the respiratory rate across a range of frequencies and is a major cause of heart rate variability in humans. The respiratory rhythm contributes to sinus arrhythmia in normal unanesthetized subjects during mechanical hyperventilation with positive pressure.

• Low-frequency oscillations{{cite journal | vauthors = Sayers BM | title = Analysis of heart rate variability | journal = Ergonomics | volume = 16 | issue = 1 | pages = 17–32 | date = January 1973 | pmid = 4702060 | doi = 10.1080/00140137308924479 }} are associated with Mayer waves (Traube–Hering–Mayer waves) of blood pressure and is usually at a frequency of 0.1 Hz, or a 10-second period.

The regulation of heart rate, encompassing both acceleration and deceleration of the heartbeat, is highly specific. Distinct patterns in heart rate can be observed during everyday movements, such as kneeling down or standing up. These patterns reflect highly sensitive physiological regulatory mechanisms that enable a healthy heart to adapt to various life influences. Heidelbach MJ, Cysarz D and Edelhäuser F (2024) Typical everyday movements cause specific patterns in heart rate. Front. Physiol. 15:1379739. doi: 10.3389/fphys.2024.1379739

Errors in the location of the instantaneous heart beat will result in errors in the calculation of the HRV. HRV is highly sensitive to artifact and errors in as low as even 2% of the data will result in unwanted biases in HRV calculations. To ensure accurate results therefore it is critical to manage artifact and RR errors appropriately prior to performing any HRV analyses.{{cite web | vauthors = Citi L, Brown EN, Barbieri R |year=2012 |title=Online tool for the detection and correction of erroneous and ectopic heartbeats |url=https://neurostat-mit.appspot.com/}}{{cite journal | vauthors = Citi L, Brown EN, Barbieri R | title = A real-time automated point-process method for the detection and correction of erroneous and ectopic heartbeats | journal = IEEE Transactions on Bio-Medical Engineering | volume = 59 | issue = 10 | pages = 2828–2837 | date = October 2012 | pmid = 22875239 | pmc = 3523127 | doi = 10.1109/TBME.2012.2211356 }}

Robust management of artifacts, including RWave identification, interpolation and exclusion requires a high degree of care and precision. This can be very time-consuming in large studies with data recorded over long durations. Software packages are able to assist users with a variety of robust and tested artifact management tools. These software programs also include some automated capability but it is important that a human review any automated artifact management and edit accordingly.

The most widely used methods can be grouped under time-domain and frequency-domain. A joint European and American task-force described standards in HRV measurements in 1996. Other methods have been proposed, such as non-linear methods.

Time-domain methods{{Cite journal| vauthors = Golgouneh A, Tarvirdizadeh B |date=2019-06-07|title=Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms|journal=Neural Computing and Applications|volume=32|issue=11|pages=7515–7537|doi=10.1007/s00521-019-04278-7|s2cid=174803224|issn=0941-0643}} are based on the beat-to-beat or NN intervals, which are analysed to give variables such as:

• SDNN (standard deviation of NN intervals). Often calculated over a 24-hour period. SDNN reflects all the cyclic components responsible for variability in the period of recording, therefore it represents total variability.
• SDANN (standard deviation of the average NN intervals) calculated over short periods, usually 5 minutes. SDANN is therefore a measure of changes in heart rate due to cycles longer than 5 minutes.
• RMSSD (root mean square of successive differences), the square root of the mean of the squares of the successive differences between adjacent NNs.
• SDSD (standard deviation of successive differences), the standard deviation of the successive differences between adjacent NNs.
• NN50, the number of pairs of successive NNs that differ by more than 50 ms.
• pNN50, the proportion of NN50 divided by total number of NNs.
• NN20, the number of pairs of successive NNs that differ by more than 20 ms.{{cite journal | vauthors = Mietus JE, Peng CK, Henry I, Goldsmith RL, Goldberger AL | title = The pNNx files: re-examining a widely used heart rate variability measure | journal = Heart | volume = 88 | issue = 4 | pages = 378–380 | date = October 2002 | pmid = 12231596 | pmc = 1767394 | doi = 10.1136/heart.88.4.378 }}
• pNN20, the proportion of NN20 divided by total number of NNs.
• EBC (estimated breath cycle), the range (max-min) within a moving window of a given time duration within the study period. The windows can move in a self-overlapping way or be strictly distinct (sequential) windows. EBC is often provided in data acquisition scenarios where HRV feedback in real time is a primary goal. EBC derived from PPG over 10-second and 16-second sequential and overlapping windows has been shown to correlate highly with SDNN.{{cite arXiv |title=Dynamic Metrics of Heart Rate Variability |vauthors=Goss CF, Miller EB |date=August 2013 |class=q-bio.QM |eprint=1308.6018}}

The series of NN intervals also can be converted into a geometric pattern such as:

Geometric Measures HRV triangular index: integral of density distribution / maximum of density distribution maximum

HRV triangular index = Number of all NN intervals / maximum number. Dependent on the length of the bin -> quote the bin size+ relative insensitive to the analytic quality of the series of NN intervals – need of reasonable number of NN intervals to generate the geometric pattern (in practice 20 min to 24 h) – not appropriate to assess short-term changes in HRV

• the sample density distribution of NN interval durations;
• sample density distribution of differences between adjacent NN intervals;
• a scatterplot of each NN (or RR) interval with the immediately preceding NN (or RR) interval[http://www.brainandheart.at/Param.php Parameter aus dem Lorenz-Plot Parameters from the Lorenz-Plot] brain & heart, "Parameter der Herzratenvariabilität" (Parameters of HRV), by Dr. Egon Winter, Austria. Accessed 2016-11-20. In German. – also called "Poincare plot" or (apparently in error[http://itl.nist.gov/div898/software/dataplot/refman1/auxillar/lorenz.htm 'Lorenz Curve'] command in online user manual for 'Dataplot' software, published by NIST, USA. Accessed via on 2016-11-20.) a "Lorenz plot"; and so forth. A simple formula is then used that judges the variability on the basis of the geometric and/or graphics properties of the resulting pattern{{citation needed|reason=Would be worth adding refs. It is not very informative to just list "whatever one could plot" – what would be interesting is what actual types of plots are used clinically|date=August 2015}}.

Frequency domain methods assign bands of frequency and then count the number of NN intervals that match each band. The bands are typically high frequency (HF) from 0.15 to 0.4 Hz, low frequency (LF) from 0.04 to 0.15 Hz, and the very low frequency (VLF) from 0.0033 to 0.04 Hz.{{cite journal | vauthors = Deka B, Deka D | title=Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective | journal= BioMedical Engineering OnLine | volume=22 | issue=1 | pages=35 | year=2023 | doi= 10.1186/s12938-023-01100-3 | doi-access=free | pmc=10103447 | pmid=37055770 }} HF power reflects stimulation by the parasympathetic nervous system (PNS), whereas LF power reflects stimulation by both the sympathetic nervous system (SNS) and the PNS. VLF power is associated with thermoregulation, the renin–angiotensin system. and peripheral vasomotor activity.

Several methods of analysis are available. Power spectral density (PSD), using parametric or nonparametric methods, provides basic information on the power distribution across frequencies. One of the most commonly used PSD methods is the discrete Fourier transform.

Methods for the calculation of PSD may be generally classified as nonparametric and parametric. In most instances, both methods provide comparable results. The advantages of the nonparametric methods are (1) the simplicity of the algorithm used (fast Fourier transform [FFT] in most of the cases) and (2) the high processing speed. The advantages of parametric methods are (1) smoother spectral components that can be distinguished independent of preselected frequency bands, (2) easy postprocessing of the spectrum with an automatic calculation of low- and high-frequency power components with an easy identification of the central frequency of each component, and (3) an accurate estimation of PSD even on a small number of samples on which the signal is supposed to maintain stationarity. The basic disadvantage of parametric methods is the need of verification of the suitability of the chosen model and of its complexity (that is, the order of the model).

In addition to classical FFT-based methods used for the calculation of frequency parameters, a more appropriate PSD estimation method is the Lomb–Scargle periodogram.{{cite journal | vauthors = Işler Y, Kuntalp M | title = Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure | journal = Computers in Biology and Medicine | volume = 37 | issue = 10 | pages = 1502–1510 | date = October 2007 | pmid = 17359959 | doi = 10.1016/j.compbiomed.2007.01.012 }} Analysis has shown that the LS periodogram can produce a more accurate estimate of the PSD than FFT methods for typical RR data. Since the RR data is an unevenly sampled data, another advantage of the LS method is that in contrast to FFT-based methods it is able to be used without the need to resample and detrend the RR data.

Alternatively, to avoid artefacts that are created when calculating the power of a signal that includes a single high-intensity peak (for example caused by an arrhythmic heart beat), the concept of the 'instantaneous Amplitude' has been introduced, which is based on the Hilbert transform of the RR data.{{cite journal | vauthors = von Rosenberg W, Chanwimalueang T, Adjei T, Jaffer U, Goverdovsky V, Mandic DP | title = Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV | journal = Frontiers in Physiology | volume = 8 | pages = 360 | date = 2017 | pmid = 28659811 | pmc = 5469891 | doi = 10.3389/fphys.2017.00360 | doi-access = free }}

A newly used HRV index,{{citation needed|reason=used by whom?|date=August 2015}} which depends on the wavelet entropy measures, is an alternative choice. The wavelet entropy measures are calculated using a three-step procedure defined in the literature. First, the wavelet packet algorithm is implemented using the Daubechies 4 (DB4) function as the mother wavelet with a scale of 7. Once the wavelet coefficients are obtained, the energy for each coefficient are calculated as described in the literature. After calculating the normalized values of wavelet energies, which represent the relative wavelet energy (or the probability distribution), the wavelet entropies are obtained using the definition of entropy given by Shannon.

Given the complexity of the mechanisms regulating heart rate, it is reasonable to assume that applying HRV analysis based on methods of non-linear dynamics will yield valuable information. Although chaotic behavior has been assumed, more rigorous testing has shown that heart rate variability cannot be described as a low dimensional chaotic process.{{cite journal | vauthors = Kanters JK, Holstein-Rathlou NH, Agner E | title = Lack of evidence for low-dimensional chaos in heart rate variability | journal = Journal of Cardiovascular Electrophysiology | volume = 5 | issue = 7 | pages = 591–601 | date = July 1994 | pmid = 7987529 | doi = 10.1111/j.1540-8167.1994.tb01300.x | s2cid = 27839503 }} However, application of chaotic globals to HRV has been shown to predict diabetes status.{{cite journal| vauthors = De Souza NM, Vanderlei LC, Garner DM |title=Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV|journal=Complexity|date=2 January 2015|volume=20|issue=3|pages=84–92|doi=10.1002/cplx.21508|bibcode=2015Cmplx..20c..84D}} The most commonly used non-linear method of analysing heart rate variability is the Poincaré plot. Each data point represents a pair of successive beats, the x-axis is the current RR interval, while the y-axis is the previous RR interval. HRV is quantified by fitting mathematically defined geometric shapes to the data.{{cite journal | vauthors = Brennan M, Palaniswami M, Kamen P | title = Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? | journal = IEEE Transactions on Bio-Medical Engineering | volume = 48 | issue = 11 | pages = 1342–1347 | date = November 2001 | pmid = 11686633 | doi = 10.1109/10.959330 | s2cid = 1397879 }} Other methods used are the correlation dimension, symbolic dynamics,{{cite journal | vauthors = Voss A, Schulz S, Schroeder R, Baumert M, Caminal P | title = Methods derived from nonlinear dynamics for analysing heart rate variability | journal = Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences | volume = 367 | issue = 1887 | pages = 277–296 | date = January 2009 | pmid = 18977726 | doi = 10.1098/rsta.2008.0232 | s2cid = 389500 | bibcode = 2009RSPTA.367..277V }} nonlinear predictability, pointwise correlation dimension,{{cite journal | vauthors = Storella RJ, Wood HW, Mills KM, Kanters JK, Højgaard MV, Holstein-Rathlou NH | title = Approximate entropy and point correlation dimension of heart rate variability in healthy subjects | journal = Integrative Physiological and Behavioral Science | volume = 33 | issue = 4 | pages = 315–320 | date = October 1998 | pmid = 10333974 | doi = 10.1007/BF02688699 | s2cid = 25332169 }} approximate entropy, sample entropy,{{cite journal | vauthors = Richman JS, Moorman JR | title = Physiological time-series analysis using approximate entropy and sample entropy | journal = American Journal of Physiology. Heart and Circulatory Physiology | volume = 278 | issue = 6 | pages = H2039–H2049 | date = June 2000 | pmid = 10843903 | doi = 10.1152/ajpheart.2000.278.6.H2039 | s2cid = 2389971 }} multiscale entropy analysis,{{cite journal | vauthors = Costa M, Goldberger AL, Peng CK | title = Multiscale entropy analysis of complex physiologic time series | journal = Physical Review Letters | volume = 89 | issue = 6 | pages = 068102 | date = August 2002 | pmid = 12190613 | doi = 10.1103/physrevlett.89.068102 | bibcode = 2002PhRvL..89f8102C | s2cid = 499639 }} sample asymmetry{{cite journal | vauthors = Kovatchev BP, Farhy LS, Cao H, Griffin MP, Lake DE, Moorman JR | title = Sample asymmetry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome | journal = Pediatric Research | volume = 54 | issue = 6 | pages = 892–898 | date = December 2003 | pmid = 12930915 | doi = 10.1203/01.pdr.0000088074.97781.4f | doi-access = free }} and memory length (based on inverse statistical analysis).{{cite journal | vauthors = Shirazi AH, Raoufy MR, Ebadi H, De Rui M, Schiff S, Mazloom R, Hajizadeh S, Gharibzadeh S, Dehpour AR, Amodio P, Jafari GR, Montagnese S, Mani AR | display-authors = 6 | title = Quantifying memory in complex physiological time-series | journal = PLOS ONE | volume = 8 | issue = 9 | pages = e72854 | date = 2013 | pmid = 24039811 | pmc = 3764113 | doi = 10.1371/journal.pone.0072854 | bibcode = 2013PLoSO...872854S | doi-access = free }}{{cite journal| vauthors = Ebadi H, Shirazi AH, Mani AR, Jafari GR |title=Inverse statistical approach in heartbeat time series|journal=Journal of Statistical Mechanics: Theory and Experiment|date=24 August 2011|volume=2011|issue=8|pages=08014|doi=10.1088/1742-5468/2011/08/P08014|bibcode=2011JSMTE..08..014E|s2cid=122326023 }} It is also possible to represent long range correlations geometrically.

Sequences of RR intervals have been found to have long-term correlations.{{cite journal | vauthors = Bailly F, Longo G, Montevil M | title = A 2-dimensional geometry for biological time | journal = Progress in Biophysics and Molecular Biology | volume = 106 | issue = 3 | pages = 474–484 | date = September 2011 | pmid = 21316386 | doi = 10.1016/j.pbiomolbio.2011.02.001 | arxiv = 1004.4186 | s2cid = 2503067 }} However, one flaw with these analyses is their lack of goodness-of-fit statistics, i.e. values are derived that may or may not have adequate statistical rigor. Different types of correlations have been found during different sleep stages.

A basic problem is that all the parameters used to characterize HRV strongly depend on heart rate,{{cite journal | vauthors = Billman GE | title = The effect of heart rate on the heart rate variability response to autonomic interventions | journal = Frontiers in Physiology | volume = 4 | date = 2013 | page = 222 | doi = 10.3389/fphys.2013.00222 | pmid = 23986716 | pmc = 3752439 | doi-access = free }} but many articles have not adjusted properly or at all for HR differences when comparing HRV in multiple circumstances.{{cite journal | vauthors = Monfredi O et al | title = Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate | journal = Hypertension | volume = 64 | pages = 1334–1343 | date = 2014 | issue = 6 | doi = 10.1161/HYPERTENSIONAHA.114.03782 | pmid = 25225208 | pmc = 4326239 | doi-access = free }}

However, the exact HRV(HR) relationship is still a matter of debate. For time domain parameters (RMSSD, SDNN, etc.) the results imply that, if there exists a universal function, it should be either exponential or hyperbolic in nature.{{cite journal | vauthors = Boyett M et al | title = CrossTalk opposing view: Heart rate variability as a measure of cardiac autonomic responsiveness is fundamentally flawed | journal = J. Physiol. | volume = 597 | pages = 2599–2601 | date = 2019| issue = 10 | doi = 10.1113/JP277501 | pmid = 31006856 | pmc = 6826226 | doi-access = free }} The evaluation procedures used to determine HRV(HR) function have not allowed to decide between these options, so far.

A new evaluation method has recently allowed to determine a HRV(HR) function with unprecedented precision:{{cite journal | vauthors = Buzas A et al | title = A Novel Approach in Heart-Rate-Variability Analysis Based on Modified Poincaré Plots | journal = IEEE Access | volume = 10 | pages = 36606–36615 | date = 2022 | doi = 10.1109/ACCESS.2022.3162234 | doi-access = free | bibcode = 2022IEEEA..1036606B }} it can be described by two descending exponential components for healthy individuals, in general.

Time domain methods are preferred to frequency domain methods when short-term recordings are investigated. This is due to the fact that the recording should be at least 10 times the wavelength of the lowest frequency bound of interest. Thus, recording of approximately 1 minute is needed to assess the HF components of HRV (i.e., a lowest bound of 0.15 Hz is a cycle of 6.6 seconds and so 10 cycles require ~60 seconds), while more than 4 minutes are needed to address the LF component (with a lower bound of 0.04 Hz).{{citation needed|date=July 2021}}

Although time domain methods, especially the SDNN and RMSSD methods, can be used to investigate recordings of long durations, a substantial part of the long-term variability is day–night differences. Thus, long-term recordings analyzed by time domain methods should contain at least 18 hours of analyzable ECG data that include the whole night.{{citation needed|date=July 2021}}

Although cardiac automaticity is intrinsic to various pacemaker tissues, heart rate and rhythm are largely under the control of the autonomic nervous system. The parasympathetic influence on heart rate is mediated via release of acetylcholine by the vagus nerve. Muscarinic acetylcholine receptors respond to this release mostly by an increase in cell membrane K+ conductance. Acetylcholine also inhibits the hyperpolarization-activated "pacemaker" current. The "Ik decay" hypothesis proposes that pacemaker depolarization results from slow deactivation of the delayed rectifier current, Ik, which, due to a time-independent background inward current, causes diastolic depolarization. Conversely, the "If activation" hypothesis suggests that after action potential termination, If provides a slowly activating inward current predominating over decaying Ik, thus initiating slow diastolic depolarization.{{citation needed|date=March 2021}}

The sympathetic influence on heart rate is mediated by release of epinephrine and norepinephrine. Activation of β-adrenergic receptors results in cAMP-mediated phosphorylation of membrane proteins and increases in ICaL and in If the result is an acceleration of the slow diastolic depolarization.

Under resting conditions, vagal tone prevails and variations in heart period are largely dependent on vagal modulation. The vagal and sympathetic activity constantly interact. Because the sinus node is rich in acetylcholinesterase, the effect of any vagal impulse is brief because the acetylcholine is rapidly hydrolyzed. Parasympathetic influences exceed sympathetic effects probably through two independent mechanisms: a cholinergically induced reduction of norepinephrine released in response to sympathetic activity, and a cholinergic attenuation of the response to an adrenergic stimulus.

The RR interval variations present during resting conditions represent beat-by-beat variations in cardiac autonomic inputs. However, efferent vagal (parasympathetic) activity is a major contributor to the HF component, as seen in clinical and experimental observations of autonomic maneuvers such as electrical vagal stimulation, muscarinic receptor blockade, and vagotomy. More problematic is the interpretation of the LF component, which was considered by some as a marker of sympathetic modulation (especially when expressed in normalized units) but is now known to include both sympathetic and vagal influences. For example, during sympathetic activation the resulting tachycardia is usually accompanied by a marked reduction in total power, whereas the reverse occurs during vagal activation. Thus the spectral components change in the same direction and do not indicate that LF faithfully reflects sympathetic effects.

HRV measures fluctuations in autonomic inputs to the heart rather than the mean level of autonomic inputs. Thus, both withdrawal and saturatingly high levels of autonomic input to the heart can lead to diminished HRV.

A reduction of HRV has been reported in several cardiovascular and noncardiovascular diseases.

Depressed HRV after MI may reflect a decrease in vagal activity directed to the heart. HRV in patients surviving an acute MI reveal a reduction in total and in the individual power of spectral components. The presence of an alteration in neural control is also reflected in a blunting of day-night variations of RR interval. In post-MI patients with a very depressed HRV, most of the residual energy is distributed in the VLF frequency range below 0.03 Hz, with only a small respiration-related variations.

In neuropathy associated with diabetes mellitus characterized by alteration in small nerve fibers, a reduction in time domain parameters of HRV seems not only to carry negative prognostic value but also to precede the clinical expression of autonomic neuropathy. In diabetic patients without evidence of autonomic neuropathy, reduction of the absolute power of LF and HF during controlled conditions was also reported. Similarly, diabetic patients can be differentiated from normal controls on the basis of reduction in HRV.

A very reduced HRV with no definite spectral components has been reported in patients with a recent heart transplant. The appearance of discrete spectral components in a few patients is considered to reflect cardiac reinnervation. This reinnervation may occur as early as 1 to 2 years after transplantation and is assumed to be of sympathetic origin. In addition, a correlation between respiratory rate and the HF component of HRV observed in some transplanted patients also indicates that a nonneural mechanism may generate a respiration-related rhythmic oscillation.

A reduced HRV has been observed consistently in patients with cardiac failure. In this condition characterized by signs of sympathetic activation such as faster heart rates and high levels of circulating catecholamines, a relation between changes in HRV and the extent of left ventricular dysfunction was reported. In fact, whereas the reduction in time domain measures of HRV seemed to parallel the severity of the disease, the relationship between spectral components and indices of ventricular dysfunction appears to be more complex. In particular, in most patients with a very advanced phase of the disease and with a drastic reduction in HRV, an LF component could not be detected despite the clinical signs of sympathetic activation. This reflects that, as stated above, the LF may not accurately reflect cardiac sympathetic tone.

Liver cirrhosis is associated with decreased HRV. Decreased HRV in patients with cirrhosis has a prognostic value and predicts mortality. Loss of HRV is also associated with higher plasma pro-inflammatory cytokine levels and impaired neurocognitive function in this patient population.{{cite journal | vauthors = Mani AR, Montagnese S, Jackson CD, Jenkins CW, Head IM, Stephens RC, Moore KP, Morgan MY | display-authors = 6 | title = Decreased heart rate variability in patients with cirrhosis relates to the presence and degree of hepatic encephalopathy | journal = American Journal of Physiology. Gastrointestinal and Liver Physiology | volume = 296 | issue = 2 | pages = G330–G338 | date = February 2009 | pmid = 19023029 | pmc = 2643913 | doi = 10.1152/ajpgi.90488.2008 }}

HRV is decreased in patients with sepsis. Loss of HRV has both diagnostic and prognostic value in neonates with sepsis.{{cite journal | vauthors = Griffin MP, Moorman JR | title = Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis | journal = Pediatrics | volume = 107 | issue = 1 | pages = 97–104 | date = January 2001 | pmid = 11134441 | doi = 10.1542/peds.107.1.97 }} The pathophysiology of decreased HRV in sepsis is not well understood but there is experimental evidence to show that partial uncoupling of cardiac pacemaker cells from autonomic neural control may play a role in decreased HRV during acute systemic inflammation.{{cite journal | vauthors = Gholami M, Mazaheri P, Mohamadi A, Dehpour T, Safari F, Hajizadeh S, Moore KP, Mani AR | display-authors = 6 | title = Endotoxemia is associated with partial uncoupling of cardiac pacemaker from cholinergic neural control in rats | journal = Shock | volume = 37 | issue = 2 | pages = 219–227 | date = February 2012 | pmid = 22249221 | doi = 10.1097/shk.0b013e318240b4be | s2cid = 36435763 | doi-access = free }} (Decreased HRV is generally lower in inflammatory conditions).

Patients with chronic complete high cervical spinal cord lesions have intact efferent vagal neural pathways directed to the sinus node. However, an LF component can be detected in HRV and arterial pressure variabilities of some tetraplegic patients. Thus, the LF component of HRV in those without intact sympathetic inputs to the heart represent vagal modulation.

Victims of sudden cardiac death have been found to have had lower HRV than healthy individuals.{{cite journal | vauthors = Mølgaard H, Sørensen KE, Bjerregaard P | title = Attenuated 24-h heart rate variability in apparently healthy subjects, subsequently suffering sudden cardiac death | journal = Clinical Autonomic Research | volume = 1 | issue = 3 | pages = 233–237 | date = September 1991 | pmid = 1822256 | doi = 10.1007/BF01824992 | s2cid = 31170353 }} HRV can be observed to be depressed prior to the development of SCD, which raises questions about whether or not altered autonomic function plays a role in the development of electrical instability. HRV is also depressed in SCD survivors, who are at high risk for subsequent episodes.{{cite journal | vauthors = Singer DH, Martin GJ, Magid N, Weiss JS, Schaad JW, Kehoe R, Zheutlin T, Fintel DJ, Hsieh AM, Lesch M | display-authors = 6 | title = Low heart rate variability and sudden cardiac death | journal = Journal of Electrocardiology | volume = 21 | issue = Suppl | pages = S46–S55 | date = January 1988 | pmid = 3063772 | doi = 10.1016/0022-0736(88)90055-6 | series = Computerized Interpretation of the Electrocardiogram XIII }} HRV is markedly decreased prior to both fatal and non-fatal arrhythmias.

HRV correlates with the progression of disease and outcome of cancer patients, according to a systematic review of published studies.{{cite journal | vauthors = Kloter E, Barrueto K, Klein SD, Scholkmann F, Wolf U | title = Heart Rate Variability as a Prognostic Factor for Cancer Survival – A Systematic Review | journal = Frontiers in Physiology | volume = 9 | pages = 623 | year = 2018 | pmid = 29896113 | pmc = 5986915 | doi = 10.3389/fphys.2018.00623 | doi-access = free }} Patients in the early stages of cancer have a significantly higher HRV when compared to patients in the later stages of cancer, suggesting disease severity influences HRV. Different ranges of HRV can be observed between cancer types.{{cite journal | vauthors = De Couck M, Gidron Y | title = Norms of vagal nerve activity, indexed by Heart Rate Variability, in cancer patients | journal = Cancer Epidemiology | volume = 37 | issue = 5 | pages = 737–741 | date = October 2013 | pmid = 23725879 | doi = 10.1016/j.canep.2013.04.016 }}

HRV alterations occur in healthy pregnancies as well as similar changes in pregnancies with gestational diabetes that include lower HRV mean values.{{cite journal | vauthors = Stein PK, Hagley MT, Cole PL, Domitrovich PP, Kleiger RE, Rottman JN | title = Changes in 24-hour heart rate variability during normal pregnancy | journal = American Journal of Obstetrics and Gynecology | volume = 180 | issue = 4 | pages = 978–985 | date = April 1999 | pmid = 10203667 | doi = 10.1016/s0002-9378(99)70670-8 }}{{cite journal | vauthors = Maser RE, Lenhard MJ, Kolm P | title = Autonomic modulation in gestational diabetes mellitus | journal = Journal of Diabetes and Its Complications | volume = 28 | issue = 5 | pages = 684–688 | date = September 2014 | pmid = 24972765 | doi = 10.1016/j.jdiacomp.2014.05.005 }}{{cite journal | vauthors = Ayaz R, Hocaoğlu M, Günay T, Yardımcı OD, Turgut A, Karateke A | title = Anxiety and depression symptoms in the same pregnant women before and during the COVID-19 pandemic | journal = Journal of Perinatal Medicine | volume = 48 | issue = 9 | pages = 965–970 | date = November 2020 | pmid = 32887191 | doi = 10.1515/jpm-2020-0380 | s2cid = 221502222 | doi-access = free }}{{cite journal | vauthors = Mizuno T, Tamakoshi K, Tanabe K | title = Anxiety during pregnancy and autonomic nervous system activity: A longitudinal observational and cross-sectional study | journal = Journal of Psychosomatic Research | volume = 99 | pages = 105–111 | date = August 2017 | pmid = 28712414 | doi = 10.1016/j.jpsychores.2017.06.006 }}{{Cite journal | vauthors = Gandhi PH, Mehta HB, Gokhale AV, Desai CB, Gokhale PA, Shah CJ |date=2014|title=A study on cardiac autonomic modulation during pregnancy by non-invasive heart rate variability measurement|url=http://ijmedph.org/article/361|journal=International Journal of Medicine and Public Health|volume=4|issue=4|pages=441–445|doi=10.4103/2230-8598.144131|doi-access=free}}{{cite journal | vauthors = Logan JG, Yeo S | title = Effects of Stretching Exercise on Heart Rate Variability During Pregnancy | journal = The Journal of Cardiovascular Nursing | volume = 32 | issue = 2 | pages = 107–111 | date = March 2017 | pmid = 26938507 | doi = 10.1097/jcn.0000000000000326 | s2cid = 3586608 }}{{cite journal | vauthors = Kimmel MC, Fransson E, Cunningham JL, Brann E, Grewen K, Boschiero D, Chrousos GP, Meltzer-Brody S, Skalkidou A | display-authors = 6 | title = Heart rate variability in late pregnancy: exploration of distinctive patterns in relation to maternal mental health | journal = Translational Psychiatry | volume = 11 | issue = 1 | pages = 286 | date = May 2021 | pmid = 33986246 | pmc = 8119957 | doi = 10.1038/s41398-021-01401-y }}

Low RMSSD, thought to represent vagal tone, have been associated with major depression.{{cite journal | vauthors = Zhou H, Dai Z, Hua L, Jiang H, Tian S, Han Y, Lin P, Wang H, Lu Q, Yao Z | display-authors = 6 | title = Decreased Task-Related HRV Is Associated With Inhibitory Dysfunction Through Functional Inter-Region Connectivity of PFC in Major Depressive Disorder | journal = Frontiers in Psychiatry | volume = 10 | pages = 989 | date = 2020-01-22 | pmid = 32038327 | pmc = 6988511 | doi = 10.3389/fpsyt.2019.00989 | doi-access = free }} Lower SDNN and elevated LF/HF were found in those with bipolar disorder, and in particular those characterized as having greater illness severity due to greater number of episodes, illness duration and whether there had been psychosis.{{cite journal | vauthors = Levy B | title = Illness severity, trait anxiety, cognitive impairment and heart rate variability in bipolar disorder | journal = Psychiatry Research | volume = 220 | issue = 3 | pages = 890–895 | date = December 2014 | pmid = 25219620 | doi = 10.1016/j.psychres.2014.07.059 | s2cid = 2713541 }}{{cite journal | vauthors = Moon E, Lee SH, Kim DH, Hwang B | title = Comparative Study of Heart Rate Variability in Patients with Schizophrenia, Bipolar Disorder, Post-traumatic Stress Disorder, or Major Depressive Disorder | journal = Clinical Psychopharmacology and Neuroscience | volume = 11 | issue = 3 | pages = 137–143 | date = December 2013 | pmid = 24465250 | pmc = 3897762 | doi = 10.9758/cpn.2013.11.3.137 }} Patients with PTSD also had lower HF, a measure of vagal tone.

Interventions that augment HRV may be protective against cardiac mortality and sudden cardiac death. Although the rationale for changing HRV is sound, it also contains the inherent danger of leading to the unwarranted assumption that modification of HRV translates directly into cardiac protection, which may not be the case. Despite the growing consensus that increases in vagal activity can be beneficial, it is not as yet known how much vagal activity (or HRV as a marker) has to increase in order to provide adequate protection.

The data on the effect of β-blockers on HRV in post-MI patients are surprisingly scant. Despite the observation of statistically significant increases, the actual changes are very modest. In conscious post-MI dogs, β-blockers do not modify HRV. The unexpected observation that before MI, β-blockade increases HRV only in the animals destined to be at low risk for lethal arrhythmias after MI may suggest novel approaches to post-MI risk stratification.

Data exist for several antiarrhythmic drugs. Flecainide and propafenone but not amiodarone were reported to decrease time domain measures of HRV in patients with chronic ventricular arrhythmia. In another study, propafenone reduced HRV and decreased LF much more than HF. A larger study confirmed that flecainide, also encainide and moricizine, decreased HRV in post-MI patients but found no correlation between the change in HRV and mortality during follow-up. Thus, some antiarrhythmic drugs associated with increased mortality can reduce HRV. However, it is not known whether these changes in HRV have any direct prognostic significance.

Low-dose muscarinic receptor blockers, such as atropine and scopolamine, may produce a paradoxical increase in vagal effects on the heart, as suggested by a decrease in heart rate. In addition, scopolamine and low dose atropine can markedly increase HRV. However, though the heart rate slowing in proportional to the (low) dose of atropine, the increase in HRV varies widely across and within individuals. This suggests that even for vagal activity to the heart, HRV may be a limited marker.

The effect of thrombolysis on HRV (assessed by pNN50) was reported in 95 patients with acute MI. HRV was higher 90 minutes after thrombolysis in the patients with patency of the infarct-related artery. However, this difference was no longer evident when the entire 24 hours were analyzed.

Exercise training may decrease cardiovascular mortality and sudden cardiac death. Regular exercise training is also thought to modify cardiac autonomic control. Individuals who exercise regularly have a 'training bradycardia' (i.e., low resting heart rate) and generally have higher HRV than sedentary individuals.{{cite journal | vauthors = Hottenrott K, Hoos O, Esperer HD | title = [Heart rate variability and physical exercise. Current status] | journal = Herz | volume = 31 | issue = 6 | pages = 544–552 | date = September 2006 | pmid = 17036185 | doi = 10.1007/s00059-006-2855-1 | s2cid = 40627250 }}

The technique called resonant breathing biofeedback teaches how to recognize and control involuntary heart rate variability. A randomized study by Sutarto et al. assessed the effect of resonant breathing biofeedback among manufacturing operators; depression, anxiety and stress significantly decreased.{{cite journal | vauthors = Sutarto AP, Wahab MN, Zin NM | title = Resonant breathing biofeedback training for stress reduction among manufacturing operators | journal = International Journal of Occupational Safety and Ergonomics | volume = 18 | issue = 4 | pages = 549–561 | year = 2012 | pmid = 23294659 | doi = 10.1080/10803548.2012.11076959 | doi-access = free }} A first overall meta-analysis by Goessl VC et al. (24 studies, 484 individuals, 2017) indicates HRV biofeedback training is associated with a large reduction in self-reported stress and anxiety, while mentioning that more well-controlled studies are needed.{{cite journal | vauthors = Goessl VC, Curtiss JE, Hofmann SG | title = The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis | journal = Psychological Medicine | volume = 47 | issue = 15 | pages = 2578–2586 | date = November 2017 | pmid = 28478782 | doi = 10.1017/S0033291717001003 | hdl-access = free | hdl = 2144/26911 | s2cid = 4710497 }}

One study that surveyed the physiological effects of playing Native American flutes found a significant HRV increase when playing both low-pitched and high-pitched flutes.{{cite arXiv |title=An Exploration of Physiological Responses to the Native American Flute |vauthors=Miller EB, Goss CF |date=January 2014 |class=q-bio.QM |eprint=1401.6004}}

Even though there are no widely accepted standard values for HRV that can be used for clinical purposes, multiple studies have measured and reported normal values for various populations:{{cite journal | vauthors = Shaffer F, Ginsberg JP | title=An Overview of Heart Rate Variability Metrics and Norms | journal= Frontiers in Public Health | volume=5 | pages=258 | year=2017 | doi= 10.3389/fpubh.2017.00258 | doi-access=free | pmid=29034226| pmc=5624990 }}{{cite journal | vauthors = Nunan D, Sandercock GR, Brodie DA | title = A quantitative systematic review of normal values for short-term heart rate variability in healthy adults | journal = Pacing and Clinical Electrophysiology | volume = 33 | issue = 11 | pages = 1407–1417 | date = November 2010 | pmid = 20663071 | doi = 10.1111/j.1540-8159.2010.02841.x | s2cid = 44378765 }}{{cite web | title = MSSD and other HRV metrics | work = Heart Rate Variability | date = 25 December 2022 | publisher = welltory.com | url = https://welltory.com/rmssd-and-other-hrv-measurements/ | accessdate = 2024-04-26 }}

Time Domain Analysis Abbreviations

IBI is InterBeat Interval, the time period between successive heartbeats (Normal-to-Normal interbeat interval, also known as the R–R interval), measured in milliseconds (ms). SDNN is Standard Deviation of Normal-to-Normal interbeat intervals measured in milliseconds. RMSSD is Root Mean Square of Successive Differences between normal heartbeats measured in milliseconds. The conventional recording time is five minutes.

Spectral Analysis Abbreviations

LF is Power of the Low Frequency range [ms squared (ms²) or normal units (nu)] HF is Power of the High Frequency range [ms squared (ms²) or normal units (nu)] LF/HF is the ratio of LF-to-HF power

• Heart rate turbulence
• Sinus rhythm

{{Reflist|30em}}

• [https://www.aapb.org/i4a/pages/index.cfm?pageid=1 Association for Applied Psychophysiology and Biofeedback]
• [http://bfe.org/ Biofeedback Federation of Europe]
• [https://www.escardio.org/static_file/Escardio/Guidelines/Scientific-Statements/guidelines-Heart-Rate-Variability-FT-1996.pdf 1996 guidelines]
• [http://jp.physoc.org/cgi/reprint/542/3/669?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=taylor&fulltext=variability&andorexactfulltext=and&searchid=1&FIRSTINDEX=0&sortspec=relevance&resourcetype=HWCIT Review on the mechanisms of cardiovascular variability] in the Journal of Physiology

{{Authority control}}

{{DEFAULTSORT:Heart Rate Variability}}

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