When a Real Heartbeat Becomes an Artifact: PACs, PVCs, and the Integrity of HR and HRV Analysis
- Fred Shaffer
- Aug 25
- 23 min read
Updated: 3 days ago
An electrocardiogram (ECG) shows the heart's electrical activity. Heart rate (HR) and heart rate variability (HRV) throw most of that picture away and keep only the timing between beats (Kligfield et al., 2007; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [Task Force], 1996). That simplification works well until the heart throws in an extra beat.
A premature atrial complex (PAC) and a premature ventricular complex (PVC) are real beats with real clinical meaning (Marcus, 2020; Mond & Haqqani, 2019). They become a measurement problem only because HRV assumes that every interval you feed it came from the sinus node (Peltola, 2012; Task Force, 1996).
PACs and PVCs represent ectopic beats, meaning they originate outside the sinoatrial node.
So one recording asks you to do two jobs at once. The clinical job asks what the heart did, and the measurement job asks how much the sinus node varied.
This post keeps those jobs separate. You will learn to recognize ectopic beats on the strip, to predict what they do to your HR and HRV numbers, to repair the analysis series without editing the raw ECG, and to spot the point where repair turns into fabrication (Laborde et al., 2017; Lipponen & Tarvainen, 2019; Peltola, 2012).
When a Real Beat Becomes an Analytic Artifact
Every recording produces two lists of numbers, and confusing them causes most HRV errors. The RR interval list holds the time between every detected R peak, whatever produced it. The NN interval list holds only the time between normal sinus beats (Task Force, 1996).
Software often labels both lists as RR, so an RR file can quietly contain ectopic beats, missed beats, extra markers, and mistimed markers (Lipponen & Tarvainen, 2019; Weinstein et al., 2026). Every standard HRV metric assumes you handed it the second list (Berntson et al., 1997; Task Force, 1996).
A PAC or PVC is never an artifact in the clinical record. It becomes one only inside an NN series, and that label never gives you permission to delete the beat from the raw ECG (Peltola, 2012; Task Force, 1996).
Keeping three layers of the same recording solves most of the problem. The raw ECG stays untouched and answers clinical questions. The annotation layer records what you or the software called each beat. The NN copy holds the estimated sinus timing you send to the HRV software (Laborde et al., 2017; Peltola, 2012).
That habit protects the patient and the statistics. A rising PAC burden can flag atrial vulnerability, and frequent PVCs deserve evaluation alongside symptoms and ventricular function (Guichard et al., 2022; Marcus, 2020). Cleaning up the HRV series should never erase the rhythm history of the person in front of you.
Spotting a PAC and a PVC on the Strip
A PAC fires early from somewhere in the atria other than the sinoatrial node (SA node), the pacemaker that normally sets the schedule (Mond & Haqqani, 2019). Because the impulse starts in the wrong place, its P wave looks different in shape or direction from its neighbors. It usually travels the normal route to the ventricles, so the QRS complex that follows looks narrow and ordinary (Mond & Haqqani, 2019).
Two variations cause trouble. If the ventricular conduction system has not yet recovered, the PAC produces a wide QRS that mimics a PVC (Mond & Haqqani, 2019). If the atrioventricular node has not recovered, no QRS follows, and you are left with a blocked PAC: an early P wave hidden inside the previous T wave (Mond & Haqqani, 2019).
The hardest PAC is not the obvious early beat. It is the blocked one, because software that watches only R peaks reads that hidden P wave as a dropped beat or a sinus pause (Mond & Haqqani, 2019; Peltola, 2012).
A conducted PAC usually resets the SA node, which pushes the next sinus beat later than scheduled (Mond & Haqqani, 2019). The result is a non-compensatory pause: measured from the beat before the PAC to the beat after it, the span falls short of two normal cycles (Mond & Haqqani, 2019). Treat that as a tendency rather than a rule, since post-ectopic timing varies.
A PVC fires early from the ventricles themselves and bypasses the normal conduction route entirely (Marcus, 2020). That detour gives you the classic picture: no P wave in front of it, a wide QRS above roughly 120 milliseconds, and ST-T changes pointing opposite the main QRS deflection (Marcus, 2020).
A PVC often leaves the SA node timing undisturbed. An intervening sinus impulse may reach the atrioventricular conduction system while it remains refractory and therefore fail to produce a conducted QRS complex; its P wave may remain visible or may be concealed within the PVC’s ST–T complex.
The next conducted sinus beat then occurs on the original sinus schedule, producing a compensatory pause in which the premature interval and the following interval together approximate two normal sinus cycles. Retrograde conduction, sinus-node resetting, and interpolated PVCs are important exceptions to this usual pattern (Marcus, 2020).
Check the exceptions before you trust that arithmetic. An interpolated PVC squeezes between two sinus beats with no pause at all, and a fusion beat blends sinus and ventricular activation into something in between (Marcus, 2020).
Telling True Ectopy From Equipment Noise
The RR tachogram, a simple plot of interval length over time, will show you a short-long pattern immediately. It will not tell you what caused it (Peltola, 2012; Weinstein et al., 2026). Ectopy, a missed R peak, a double-counted T wave, and a patient shifting on the table can all produce similar spikes. Look at the waveform underneath every candidate before you decide (Laborde et al., 2017; Peltola, 2012).
An interval outlier is a question, not a diagnosis. The P wave, the width of the QRS, and the length of the pause supply the answer (Marcus, 2020; Mond & Haqqani, 2019).
Detector errors leave three arithmetic signatures, and each one calls for a different fix. A missed beat leaves one interval roughly twice as long as its neighbors. A false extra marker leaves two short intervals that add up to one normal interval (Lipponen & Tarvainen, 2019). A marker placed slightly off inside the QRS keeps the beat count correct but the timing wrong, which still shifts your HRV results (Weinstein et al., 2026).
Movement and poor electrode contact look different again. They usually distort several beats in a row, shift the baseline, and produce shapes that no cardiac event would make (Friesen et al., 1990; Kligfield et al., 2007).
Repeating Ectopy
Bigeminy, trigeminy, and couplets are examples of repeating ectopy.
Bigeminy alternates sinus and ectopic beats.
Trigeminy places an ectopic beat after every two sinus beats, and both can fill an interval plot with regular oscillations that look autonomic and are not (Marcus, 2020; Peltola, 2012; Zhao et al., 2021).
A couplet is a pair of consecutive ectopic beats occurring back-to-back with no intervening sinus beat. The term may describe either atrial or ventricular ectopy, although ventricular couplets are especially important clinically.
Two successive PVCs constitute a ventricular couplet, and exactly three successive PVCs may be called a ventricular triplet.
Under the contemporary ESC definition, three or more consecutive ventricular beats at a rate above 100 bpm also constitute ventricular tachycardia; a self-terminating episode lasting less than 30 seconds is classified as nonsustained ventricular tachycardia (Zeppenfeld et al., 2022).
Each component beat carries the usual PVC signature: a premature abnormal QRS complex, typically 120 ms or longer, with a broad T wave directed opposite the major QRS deflection and no preceding P wave (Zeppenfeld et al., 2022).
A PVC is ordinarily followed by a full compensatory pause because the sinus node timing is not interrupted, in contrast to premature atrial contractions, which usually reset the sinoatrial node and produce an incomplete pause (Sattar & Hashmi, 2025).
Couplets can be further characterized by their coupling behavior. The coupling interval is measured from the preceding sinus beat to the first premature ventricular complex. When this interval remains relatively stable across ectopic events, the PVCs have fixed coupling; when it changes appreciably from event to event, they have variable coupling (Sattar & Hashmi, 2025).
The terms triplet and ventricular tachycardia overlap rather than marking mutually exclusive categories. A sequence of exactly three consecutive PVCs may be described morphologically as a ventricular triplet while also meeting the ESC definition of ventricular tachycardia if its rate exceeds 100 bpm. Authors should state the rate, number of beats, duration, and terminology they apply rather than implying that ventricular tachycardia necessarily requires more than three beats (Zeppenfeld et al., 2022).
Prognostic significance depends heavily on clinical context. Occasional idiopathic PVCs in a person without structural heart disease are often benign, but frequent PVCs should not automatically be regarded as harmless.
A sufficiently high PVC burden can cause or worsen ventricular dysfunction, including PVC-induced cardiomyopathy, even when ventricular structure and function were initially normal.
Symptoms, ectopic burden, QRS morphology, complexity, exercise response, and ventricular function should therefore guide further evaluation (Marcus, 2020; Zeppenfeld et al., 2022).
Implications for HRV Analysis
Heart rate variability is defined on normal-to-normal intervals, meaning R-R intervals arising from sinus node depolarizations only (Peltola, 2012; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Ectopic beats therefore act as physiological artifacts rather than signals. Left unedited, they bias the power spectrum by inflating higher frequency bands and produce erroneously elevated standard deviations of the R-R intervals (Peltola, 2012).
Couplets are more disruptive than isolated ectopics for a structural reason. The standard editing target for an isolated PVC is the ectopic beat together with the compensatory pause that follows it (Peltola, 2012), which amounts to two corrupted intervals.
A couplet adds the interval between the two ectopic beats, yielding at least three consecutive intervals that are not normal-to-normal intervals. This consecutiveness makes simple correction difficult.
Interpolation may be defensible when ectopic beats are isolated and the surrounding sinus rhythm provides enough information to estimate the missing timing, but large or recurrent disturbances cannot reasonably be reconstructed because interpolation introduces artificial slopes, flat stretches, or oscillations.
When frequent ectopy persists over a longer stretch, generally exclude the affected segment rather than repair it through interval-by-interval deletion or extensive interpolation (Peltola, 2012).
When a series contains many or recurrent artifacts, the recommendation is to eliminate the affected segments entirely, or to reject the recording (Peltola, 2012).
Short recordings are especially unforgiving. With short-term RR interval data, editing even a small proportion of intervals, under 5%, can affect HF and LF spectral components as well as pNN50 and RMSSD (Peltola, 2012; Salo et al., 2001).
Peltola (2012) noted that many studies historically required at least 80% normal RR intervals, particularly for frequency-domain analysis, but this is not a universally validated adequacy threshold and should not be interpreted as permission to reconstruct 20% of a five-minute record.
Because RMSSD and pNN50 are particularly sensitive to beat-to-beat errors, values computed from segments containing uncorrected couplets or extensive correction should not be interpreted as reliable autonomic indices (Shaffer & Ginsberg, 2017).
Poincaré plots
A Poincaré plot, which plots each interval against the next one, gives you a fast visual screen. Isolated outliers and ectopic clusters jump out of the main cloud of points (Sassi et al., 2015). Use it to audit the series before and after correction rather than to classify the rhythm, because different errors can land in the same region of the plot.
Why an Average Heart Rate Can Mislead You
Instantaneous HR equals 60,000 divided by the RR interval in milliseconds, so a short beat spikes the rate, and the pause that follows drops it (Task Force, 1996). Because rate is the reciprocal of interval length, averaging rates and averaging intervals give you different answers (Berntson et al., 1997). Ectopy turns that algebraic detail into a clinical one.
A fully compensatory PVC can preserve total elapsed time across the short interval and subsequent pause. Count-over-time mean HR and the reciprocal of the mean RR interval may therefore remain unchanged, while an unweighted arithmetic average of the interval-specific instantaneous rates can be biased upward.
A normal-looking mean HR still does not establish that the underlying beat series was free of ectopy (Berntson et al., 1997; Task Force, 1996).
Consider a steady RR interval of 1,000 milliseconds, corresponding to 60 beats per minute. A PVC-related pair of 600 and 1,400 milliseconds still totals 2,000 milliseconds, so the mean RR interval remains 1,000 milliseconds and the elapsed-time mean HR remains 60 beats per minute. The two interval-specific instantaneous rates are 100 and 42.9 beats per minute, however, and their unweighted arithmetic mean is 71.4.
Thus, a report will show 71.4 beats per minute only if the software averages the interval-specific rates without weighting them by duration. The discrepancy reflects the calculation method, not an actual increase in mean HR.
Conduction and detection events affect HR estimates differently. A blocked PAC produces no ventricular depolarization, so the ventricular beat count is genuinely lower during the resulting pause; the analytic problem is that software examining only R peaks may misclassify the event as sinus slowing, sinus arrest, or a missed QRS complex. A false extra marker artificially raises the detected beat count, whereas a missed QRS artificially lowers it (Lipponen & Tarvainen, 2019; Mond & Haqqani, 2019).
Three habits prevent the error. State in your report how you derived mean HR. Take it from the same series that answers your clinical or research question (Laborde et al., 2017). Report ectopic burden as its own number rather than letting it vanish into an average.
Why HRV Metrics Overreact to a Single Beat
HRV metrics exist to magnify differences between intervals, which is exactly why a few bad intervals can swamp them (Choi & Shin, 2018; Stapelberg et al., 2018). SDNN, the standard deviation of the accepted intervals, widens because the short beat and the long pause stretch the distribution (Peltola, 2012).
The RMSSD and pNN50 react even more strongly because they measure the jump from each beat to the next, and one ectopic beat creates three abnormal jumps (Bourdillon et al., 2022; Choi & Shin, 2018).
One ectopic beat corrupts several intervals and several beat-to-beat differences. Treating it as one bad number understates the damage (Bourdillon et al., 2022; Choi & Shin, 2018).
Run the numbers on a sequence of 1,000, 1,000, 600, 1,400, 1,000, and 1,000 milliseconds. The beat-to-beat differences are 0, −400, 800, −400, and 0 milliseconds. RMSSD comes out near 438 milliseconds, and pNN50 reaches 60%, from a rhythm whose sinus beats never varied at all.
Published findings illustrate how strongly results depend on the type of disturbance. Bourdillon et al. (2022) introduced a single artificial RR-interval artifact and observed RMSSD increases of 413% in the supine position and 269% in the standing position, whereas LF and HF changed by only −3% to +8%.
Choi and Shin (2018) simulated missed and false-detected QRS events rather than physiological PACs or PVCs and found statistically significant distortion across time-domain, frequency-domain, and nonlinear measures even when the simulated detection-error rate was below 1% without interpolation.
These findings demonstrate sensitivity to introduced timing and detection errors; they should not be interpreted as showing that every physiological ectopic beat produces the same magnitude or direction of change.
Your cleanup settings matter just as much as the ectopy itself. Bassi-Dibai et al. (2026) showed that the strictest correction filter in a widely used analysis package changed time-domain, frequency-domain, and nonlinear results in the same recordings. That finding makes your filter setting part of your method, not a technical footnote.
Frequency-domain and nonlinear measures are no safer. The abrupt short-long pair injects energy that no physiological rhythm produces, which can inflate high-frequency power (HF) and shift low-frequency power (LF) (Clifford & Tarassenko, 2005; Peltola, 2012; Zhao et al., 2021).
Ectopic points also stretch the Poincaré cloud and distort SD1, SD2, and entropy measures (Choi & Shin, 2018; Rincon Soler et al., 2018; Tarkiainen et al., 2007). Short recordings suffer most, because every damaged interval claims a larger share of the data (Peltola, 2012; Salo et al., 2001).
One post-ectopic effect is genuine physiology rather than error. A PVC can trigger heart rate turbulence (HRT), a brief speeding of the sinus rhythm followed by a slower return, driven by the baroreflex response to a mechanically weak ectopic contraction (Bauer et al., 2008).
Decide in advance whether you will exclude that recovery sequence, model it, or study it in its own right, rather than smoothing it away (Bauer et al., 2008; Peltola, 2012).
Why Deleting the Beat Backfires
Deleting an ectopic QRS from the raw ECG is never acceptable, because the beat happened and may matter to the patient (Guichard et al., 2022; Marcus, 2020). Excluding its timing from an NN series is a different act, and your methods section should say clearly which one you did.
Deleting an interval does not restore sinus rhythm. It shortens the record, shifts the time axis, and stitches together two beats that were never neighbors in the heart (Clifford & Tarassenko, 2005; Peltola, 2012).
Frequency-domain analysis suffers most, because spectral estimates depend on continuous, correctly spaced timing (Clifford & Tarassenko, 2005; Salo et al., 2001). Deletion also damages RMSSD and pNN50 whenever it leaves a long interval behind or creates a false adjacency between distant beats (Peltola, 2012; Salo et al., 2001).
Interpolation, which estimates the missing sinus intervals from the beats around them, is usually the better choice, although it is not neutral. Linear, nearest-neighbor, spline, and model-based methods each make different assumptions about what the sinus node would have done (Choi & Shin, 2018; Lippman et al., 1994; Rincon Soler et al., 2018). Pick the method that biases your chosen metric least, and name it in your write-up.
An isolated compensatory PVC gives you an unusual advantage. Its short interval and pause together span about two sinus cycles, so a repair can redistribute that elapsed time into two estimated sinus intervals and keep the clock honest (Mateo & Laguna, 2003; Peltola, 2012). A PAC that resets the sinus node offers no such anchor, so its replacement values remain an estimate rather than a recovered measurement.
A Correction Workflow You Can Defend
Start before the recording. Prepare the skin, secure the electrodes and cables, record more than one lead when you can, and use a sampling rate fast enough to time R peaks precisely (Kligfield et al., 2007; Laborde et al., 2017). No filter rescues a poor recording, and detector accuracy falls as signal quality falls (Friesen et al., 1990; Weinstein et al., 2026).
Flag candidates and then verify them against the ECG. A fixed deviation rule, such as flagging intervals that differ from neighboring intervals by a prespecified percentage, can identify suspects efficiently, but no single percentage is universally optimal.
A threshold that is too permissive will miss ectopy or detector errors, whereas an overly restrictive threshold may flag genuine respiratory sinus arrhythmia or rapid physiological rate changes.
Treat any threshold as a protocol-specific screening rule rather than a final classification, and verify every flagged event against the ECG waveform whenever the recording permits (Choi & Shin, 2018; Lipponen & Tarvainen, 2019).
A defensible pipeline asks two questions in order. Did the software find the R peak correctly, and was that beat a normal sinus beat? Fixing detection answers the first, and labeling ectopy answers the second (Lipponen & Tarvainen, 2019; Weinstein et al., 2026).
Repair each problem based on its cause. Add the missing R-peak time and split the long interval when a beat was missed. Delete the false marker and merge the two short intervals when noise or a T wave was counted as a beat (Lipponen & Tarvainen, 2019). Move a misplaced marker rather than changing the beat count, because timing error alone shifts HRV values (Weinstein et al., 2026).
Handle ectopy only in the NN copy. Label each PAC and PVC in the beat file, identify every interval that is not bounded by two sinus beats, and replace only those (Peltola, 2012; Task Force, 1996). Choose a method that preserves elapsed time and estimates the missing sinus timing from the accepted intervals around it (Mateo & Laguna, 2003; Peltola, 2012).
Then check your work visually. Put the original and corrected tachograms side by side, look at the Poincaré plots, and return to the ECG at each repair (Sassi et al., 2015). A good correction removes the ectopic distortion without leaving a step, an oscillation, or an unnaturally flat stretch behind.
Document the recipe as part of your method. Report the device; the leads; the sampling rate; the detector; the flagging rule; the review procedure; the counts and percentages of PACs, PVCs, detector errors, and noisy intervals; the replacement method; the excluded segments; the final analyzable duration; and any sensitivity analysis (Laborde et al., 2017; Peltola, 2012). Keep both the uncorrected and the corrected series so another analyst can retrace your steps.
Knowing When to Stop Correcting
Interpolation works when the damage is isolated, and the surrounding rhythm is stable enough to model (Peltola, 2012). It stops working as ectopy becomes frequent, clustered, or varied in shape (Peltola, 2012; Tarkiainen et al., 2007). Bigeminy, trigeminy, couplets, ventricular runs, and sustained atrial arrhythmia often leave too few real sinus intervals to rebuild anything honest.
When the algorithm must invent more sinus rhythm than the ECG actually recorded, stop correcting. Exclude the segment, say why, and report the arrhythmia burden as a finding in its own right (Peltola, 2012; Task Force, 1996).
Do not expect a universal cutoff. Published thresholds differ because the metric, record length, ectopic pattern, correction method, and population all change the answer (Bassi-Dibai et al., 2026; Choi & Shin, 2018; Stapelberg et al., 2018). Set a rule in advance and justify it for your own metric and design.
Short recordings need the strictest scrutiny, since one bad event claims a large share of a 5-minute window (Peltola, 2012; Salo et al., 2001). Longer recordings let you drop segments, but posture, activity, and sleep introduce instability of their own (Task Force, 1996). Choose clean, comparable epochs rather than trusting length alone.
Finally, test whether your conclusion depends on the cleanup. Compute the primary metric on your corrected series, on one reasonable alternative, and on a clean uncorrected stretch when you have one. Report any instability as a result, because it tells you that the autonomic interpretation rests on your correction choices.
Protecting the Clinical Picture
Ectopy is more than a signal nuisance. A rising PAC burden is associated with atrial fibrillation, stroke, and atrial cardiomyopathy, although burden thresholds still vary between studies (Guichard et al., 2022). Frequent PVCs can accompany symptoms and ventricular dysfunction in some patients, while occasional PVCs in a structurally normal heart may need only reassurance (Marcus, 2020).
The cleanest HRV record is rarely the most complete clinical record. Keep the ectopic counts, the shapes, the patterns, and the symptoms even when your HRV copy drops their timing (Guichard et al., 2022; Marcus, 2020).
One recording then supports two honest analyses. The rhythm report says what the heart did, including ectopic beats. The HRV report estimates variation among the sinus beats you accepted (Peltola, 2012; Task Force, 1996). Neither should be presented as the other.
Read the resulting numbers carefully. An inflated RMSSD or HF value in a record with ectopy is not evidence of strong vagal tone, and a drop after filtering is not evidence of autonomic change (Bassi-Dibai et al., 2026; Choi & Shin, 2018). Compare HRV against the raw rhythm, respiration, posture, activity, and medications before you interpret it (Laborde et al., 2017; Task Force, 1996).
Referral follows the patient rather than the preprocessing problem. Syncope, exertional symptoms, complex or sustained ventricular ectopy, and evidence of structural disease deserve cardiac evaluation whether or not anyone plans an HRV analysis (Guichard et al., 2022; Marcus, 2020). Artifact correction is not treatment, and it should never delay a rhythm workup.
Conclusion: Preserve the Rhythm, Reconstruct the Analysis
A real beat can be wrong for one measurement without being false data. PACs and PVCs distort HR and HRV whenever your target is sinus variability, because their early timing, pauses, and recovery break the assumption behind the metric (Bauer et al., 2008; Choi & Shin, 2018; Peltola, 2012). Heavier smoothing does not fix that. Looking at the waveform, matching each repair to its cause, and writing down what you did does fix it.
Preserve the raw ECG, preserve the ectopy, and make every replaced interval traceable to a decision you can explain. That habit protects the patient, the physiology, and the analysis (Laborde et al., 2017; Peltola, 2012).
The workflow that survives review keeps the clinical rhythm intact, fixes detection errors according to how they happened, replaces isolated ectopic intervals only when the surrounding rhythm supports an estimate, and rejects segments that demand too much invention (Lipponen & Tarvainen, 2019; Peltola, 2012). It also asks whether the conclusion holds under a different reasonable cleanup. Those steps turn artifact correction from a hidden software setting into a visible part of your method.
Five Key Takeaways
1. PACs and PVCs are real beats. They become artifacts only inside an NN series built to measure sinus variability, so preserve them in the raw ECG and in your rhythm annotations.
2. Look at the waveform before you touch the intervals. An RR outlier cannot tell you whether it came from a PAC, a PVC, a missed beat, an extra marker, or patient movement, and each of those calls for a different repair.
3. One ectopic event can distort many numbers. A single short–long interval pair corrupts two RR intervals and three successive beat-to-beat differences, potentially inflating RMSSD, SDNN, spectral power, and nonlinear indices far beyond the variability of the underlying sinus rhythm.
4. Replace rather than delete. For isolated ectopy, use a time-preserving replacement in a separate NN copy, and repair missed, extra, or misplaced detections according to what actually went wrong.
5. Stop when correction becomes invention. Exclude heavily contaminated segments from standard HRV, report ectopic burden separately, and disclose every threshold, replacement method, and sensitivity check.
Glossary
aberrant conduction: supraventricular activation that reaches part of the ventricular conduction system while it is refractory, producing a widened or altered QRS complex.
afterdepolarization: a secondary depolarization during or after a cardiac action potential that can trigger a premature impulse.
automaticity: the intrinsic capacity of cardiac cells to depolarize spontaneously and initiate electrical impulses. Normal SA-node pacemaking is an expression of automaticity; enhanced or abnormal automaticity in cells outside the SA node can generate ectopic impulses.
bigeminy: a repeating rhythm in which every other beat is ectopic.
blocked PAC: a premature atrial depolarization that fails to conduct through the atrioventricular node and therefore produces no following QRS complex.
compensatory pause: a post-ectopic pause in which the premature interval plus the following interval approximates two baseline sinus cycles, usually because the sinus node was not reset.
couplet: two consecutive ectopic beats.
coupling interval: the time from the preceding reference beat to a premature beat.
ECG: a surface recording of cardiac electrical potential over time.
ectopic beat: a cardiac impulse that originates outside the expected sinus pacemaker sequence.
fusion beat: a ventricular complex produced by simultaneous or near-simultaneous activation from a sinus impulse and a ventricular ectopic impulse.
HF: high-frequency spectral power, conventionally measured from 0.15 to 0.40 Hz in adult short-term HRV analysis.
HR: heart rate, commonly expressed as beats per minute and derivable from beat count or from the reciprocal of interval duration.
HRT: heart rate turbulence, the characteristic sinus acceleration and deceleration after an isolated PVC that reflects baroreflex responses.
HRV: heart rate variability, the variation in intervals between successive heartbeats, usually analyzed from accepted NN intervals in sinus-rhythm studies.
interpolated PVC: a PVC inserted between two conducted sinus beats without the usual compensatory pause.
interpolation: estimation of one or more missing or rejected interval values from neighboring observations or from a timing model.
LF: low-frequency spectral power, conventionally measured from 0.04 to 0.15 Hz in adult HRV analysis.
NN interval: the interval between two consecutive normal sinus beats accepted for HRV analysis.
noncompensatory pause: a post-PAC pause for which the interval spanning the premature beat is shorter than two expected sinus cycles, usually because the PAC reset the sinus node.
P wave: the ECG deflection that represents atrial depolarization.
PAC: premature atrial complex, an early atrial depolarization that begins outside the SA node.
pNN50: the percentage of consecutive accepted NN interval pairs that differ by more than 50 milliseconds.
Poincaré plot: a scatterplot of each interval against the next interval, used to visualize beat-to-beat structure and possible outliers.
PVC: premature ventricular complex, an early ventricular depolarization arising outside the normal sinus-to-His-Purkinje activation sequence.
QRS complex: the ECG waveform produced primarily by ventricular depolarization.
reentry: repetitive activation caused by an impulse circulating through tissue with suitable conduction delay and refractory properties.
respiratory sinus arrhythmia: the cyclic shortening and lengthening of sinus intervals associated with respiration.
RMSSD: the square root of the mean squared differences between consecutive NN intervals, a short-term time-domain HRV measure.
RR interval: the time between consecutive detected R peaks, whether the associated beats are normal, ectopic, or falsely detected.
RR tachogram: a time-ordered display of RR interval duration across a recording .
SA node: sinoatrial node, the usual cardiac pacemaker that initiates sinus rhythm.
SD1: the Poincaré-plot standard deviation perpendicular to the line of identity, commonly interpreted as a short-term beat-to-beat variability index.
SD2: the Poincaré-plot standard deviation along the line of identity, reflecting longer-term variability within the analyzed record.
SDNN: the standard deviation of accepted NN intervals over the analyzed period.
sinus rhythm: a cardiac rhythm initiated by the SA node with expected atrial and ventricular conduction.
stationarity: the condition in which the statistical properties of a signal remain sufficiently stable across the analyzed interval.
trigeminy: a repeating rhythm in which an ectopic beat follows every two sinus beats.
triggered activity: premature impulse formation caused by afterdepolarizations linked to the preceding action potential.
ventricular couplet: two consecutive premature ventricular complexes without an intervening sinus beat. ventricular tachycardia (VT): three or more consecutive ventricular beats at a rate exceeding 100 bpm; episodes lasting less than 30 seconds are nonsustained VT.
References
Bassi-Dibai, D., Santos-de-Araújo, A. D., Rocha, D. S., de Almeida, L. V., Figueiredo, J. K., Gondim, L. A. R., Diniz, M. R. de F., Frutuoso, V. P., Maia, M. C., Santos, P. M., & Borghi-Silva, A. (2026). Threshold-based artefact correction methods influence heart rate variability measurements in individuals with type 2 diabetes mellitus. Scientific Reports, 16, Article 11341. https://doi.org/10.1038/s41598-026-42255-y
Bauer, A., Malik, M., Schmidt, G., Barthel, P., Bonnemeier, H., Cygankiewicz, I., Guzik, P., Lombardi, F., Müller, A., Oto, A., Schneider, R., Watanabe, M., Wichterle, D., & Zareba, W. (2008). Heart rate turbulence: Standards of measurement, physiological interpretation, and clinical use. Journal of the American College of Cardiology, 52(17), 1353–1365. https://doi.org/10.1016/j.jacc.2008.07.041
Berntson, G. G., Bigger, J. T., Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., Nagaraja, H. N., Porges, S. W., Saul, J. P., Stone, P. H., & van der Molen, M. W. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34(6), 623–648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x
Bourdillon, N., Yazdani, S., Vesin, J.-M., Schmitt, L., & Millet, G. P. (2022). RMSSD is more sensitive to artifacts than frequency-domain parameters: Implication in athletes’ monitoring. Journal of Sports Science and Medicine, 21(2), 260–266. https://doi.org/10.52082/jssm.2022.260
Choi, A., & Shin, H. (2018). Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Frontiers in Physiology, 9, Article 922. https://doi.org/10.3389/fphys.2018.00922
Clifford, G. D., & Tarassenko, L. (2005). Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Transactions on Biomedical Engineering, 52(4), 630–638. https://doi.org/10.1109/TBME.2005.844028
Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., & Nagle, H. T. (1990). A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 37(1), 85–98. https://doi.org/10.1109/10.43620
Guichard, J.-B., Guasch, E., Roche, F., Da Costa, A., & Mont, L. (2022). Premature atrial contractions: A predictor of atrial fibrillation and a relevant marker of atrial cardiomyopathy. Frontiers in Physiology, 13, Article 971691. https://doi.org/10.3389/fphys.2022.971691
Hoogendijk, M. G., Géczy, T., Yap, S.-C., & Szili-Torok, T. (2020). Pathophysiological mechanisms of premature ventricular complexes. Frontiers in Physiology, 11, Article 406. https://doi.org/10.3389/fphys.2020.00406
Kligfield, P., Gettes, L. S., Bailey, J. J., Childers, R., Deal, B. J., Hancock, E. W., van Herpen, G., Kors, J. A., Macfarlane, P., Mirvis, D. M., Pahlm, O., Rautaharju, P., & Wagner, G. S. (2007). Recommendations for the standardization and interpretation of the electrocardiogram: Part I: The electrocardiogram and its technology. Circulation, 115(10), 1306–1324. https://doi.org/10.1161/CIRCULATIONAHA.106.180200
Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research: Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, Article 213. https://doi.org/10.3389/fpsyg.2017.00213
Lippman, N., Stein, K. M., & Lerman, B. B. (1994). Comparison of methods for removal of ectopy in measurement of heart rate variability. American Journal of Physiology-Heart and Circulatory Physiology, 267(1), H411–H418. https://doi.org/10.1152/ajpheart.1994.267.1.H411
Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of Medical Engineering & Technology, 43(3), 173–181. https://doi.org/10.1080/03091902.2019.1640306
Marcus, G. M. (2020). Evaluation and management of premature ventricular complexes. Circulation, 141(17), 1404–1418. https://doi.org/10.1161/CIRCULATIONAHA.119.042434
Mateo, J., & Laguna, P. (2003). Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Transactions on Biomedical Engineering, 50(3), 334–343. https://doi.org/10.1109/TBME.2003.808831
Mond, H. G., & Haqqani, H. M. (2019). The electrocardiographic footprints of atrial ectopy. Heart, Lung and Circulation, 28(10), 1463–1471. https://doi.org/10.1016/j.hlc.2019.03.005
Peltola, M. A. (2012). Role of editing of R-R intervals in the analysis of heart rate variability. Frontiers in Physiology, 3, Article 148. https://doi.org/10.3389/fphys.2012.00148
Rincon Soler, A. I., Silva, L. E. V., Fazan, R., Jr., & Murta, L. O., Jr. (2018). The impact of artifact correction methods of RR series on heart rate variability parameters. Journal of Applied Physiology, 124(3), 646–652. https://doi.org/10.1152/japplphysiol.00927.2016
Salo, M. A., Huikuri, H. V., & Seppänen, T. (2001). Ectopic beats in heart rate variability analysis: Effects of editing on time and frequency domain measures. Annals of Noninvasive Electrocardiology, 6(1), 5–17. https://doi.org/10.1111/j.1542-474X.2001.tb00080.x
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., & Yamamoto, Y. (2015). Advances in heart rate variability signal analysis: Joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace, 17(9), 1341–1353. https://doi.org/10.1093/europace/euv015
Sattar, Y., & Hashmi, M. F. (2025). Premature ventricular complex. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK547713/
Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, Article 258. https://doi.org/10.3389/fpubh.2017.00258
Stapelberg, N. J. C., Neumann, D. L., Shum, D. H. K., McConnell, H., & Hamilton-Craig, I. (2018). The sensitivity of 38 heart rate variability measures to the addition of artifact in human and artificial 24-hour cardiac recordings. Annals of Noninvasive Electrocardiology, 23(1), Article e12483. https://doi.org/10.1111/anec.12483
Tarkiainen, T. H., Kuusela, T. A., Tahvanainen, K. U. O., Hartikainen, J. E. K., Tiittanen, P., Timonen, K. L., & Vanninen, E. J. (2007). Comparison of methods for editing of ectopic beats in measurements of short-term non-linear heart rate dynamics. Clinical Physiology and Functional Imaging, 27(2), 126–133. https://doi.org/10.1111/j.1475-097X.2007.00726.x
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
Weinstein, A., Rodiño, J., & Otero, M. (2026). Effects of electrocardiogram QRS detection algorithms in heart rate variability metrics. Scientific Reports, 16, Article 18589. https://doi.org/10.1038/s41598-026-49215-6
Zeppenfeld, K., Tfelt-Hansen, J., de Riva, M., Winkel, B. G., Behr, E. R., Blom, N. A., Charron, P., Corrado, D., Dagres, N., de Chillou, C., Eckardt, L., Friede, T., Haugaa, K. H., Hocini, M., Lambiase, P. D., Marijon, E., Merino, J. L., Peichl, P., Priori, S. G., ... ESC Scientific Document Group. (2022). 2022 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. European Heart Journal, 43(40), 3997–4126. https://doi.org/10.1093/eurheartj/ehac262
Zhao, L., Li, P., Li, J., & Liu, C. (2021). Influence of ectopic beats on heart rate variability analysis. Entropy, 23(6), Article 648. https://doi.org/10.3390/e23060648
About the Author
Fred Shaffer earned his PhD in Psychology from Oklahoma State University. He earned BCIA certifications in Biofeedback and HRV Biofeedback. Fred is an Allen Fellow and Professor of Psychology at Truman State University, where he has taught for 50 years. He is a Biological Psychologist who consults and lectures in heart rate variability biofeedback, Physiological Psychology, and Psychopharmacology. Fred helped to edit Evidence-Based Practice in Biofeedback and Neurofeedback (3rd and 4th eds.) and helps to maintain BCIA's certification programs. He is a recipient of AAPB's Distinguished Scientist Award and BFE's Lifetime Impact Award.
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