Is Very-High-Frequency (VHF) HRV Real or Artifact?

The Enigma of Very-High-Frequency Heart Rate Variability

The analysis of heart rate variability (HRV), a measure of the naturally occurring beat-to-beat fluctuations in heart rate, has become a cornerstone for noninvasively assessing the autonomic nervous system (ANS). 

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The Enigma of Very-High-Frequency Heart Rate Variability

The ANS is the body's subconscious control system, regulating vital functions like breathing, digestion, and, critically, heart rate through its two main branches: the sympathetic ("fight-or-flight") and parasympathetic ("rest-and-digest") systems. 

One of the most powerful methods for interpreting these fluctuations is frequency-domain analysis. This technique acts like a mathematical prism, taking the complex time series of heartbeats and separating it into its constituent rhythms, revealing how much power or intensity is present at different frequencies.

For decades, clinicians and researchers have relied on a standardized set of frequency bands, established by a joint European and North American task force in 1996. These include the very-low-frequency (VLF) band (below 0.04 Hz), whose meaning is still debated but is thought to relate to hormonal and thermoregulatory influences; the low-frequency (LF) band (approximately 0.04–0.15 Hz), which reflects a mix of both sympathetic and parasympathetic activity and is closely tied to the body's blood pressure regulation system (the baroreflex); and the high-frequency (HF) band (approximately 0.15–0.4 Hz).

The HF band is the most well-understood component, directly reflecting the influence of the parasympathetic system via the vagus nerve, and is primarily generated by the heart rate changes associated with breathing, a phenomenon known as respiratory sinus arrhythmia (RSA) (Ernst, 2017; Task Force, 1996). RSA graphic © BioSource Software, LLC.

Caption: A flexible respiratory band tracing is shown in blue, and the ECG is rendered in red. In respiratory sinus arrhythmia, heart rate speeds during inhalation (the R-spikes are closer together) and slows during exhalation (the R-spikes are farther apart).

More recently, the advancement of high-fidelity sensors and sophisticated signal processing techniques, such as the Hilbert-Huang Transform, has allowed for the investigation of a very-high-frequency (VHF) band, typically defined as oscillations occurring between 0.4 and 0.9 Hz (Chang et al., 2014).

This band lies beyond the traditional boundaries of autonomic influence and was not included in the original standards. Its discovery has sparked a contentious debate within the scientific community: does the VHF band represent a genuine, previously undiscovered physiological process, or is it merely an artifact—a phantom signal created by measurement noise, processing errors, or mathematical echoes of the more powerful, lower-frequency rhythms? (Hayano & Yuda, 2019).

For clinicians, untangling this debate is essential for interpreting advanced HRV analyses and determining if this emerging metric holds any true relevance to patient care.

Is It Real or an Artifact?

A significant portion of the scientific community views VHF power with deep skepticism, arguing that it is largely, if not entirely, an artifactual byproduct of data collection and analysis. This perspective is grounded in several strong, interlocking arguments.

First and foremost, the VHF band is exquisitely sensitive to measurement noise. The entire enterprise of HRV analysis depends on the precise timing of the R-wave, the prominent peak in the electrocardiogram (ECG) that marks a heartbeat. Even minuscule errors in detecting this peak, known as jitter, can introduce random, high-frequency fluctuations into the beat-to-beat interval data.

While these tiny timing errors have a negligible effect on the slow-moving LF and HF bands, they can be magnified in the frequency domain to create substantial, yet entirely spurious, power in the VHF range (Berntson et al., 1997). This issue is compounded by other sources of noise, such as electrical interference from muscle tremors (electromyographic or EMG interference).

Second, methodological choices in how the data is processed can profoundly influence, or even create, VHF power. A critical parameter is the sampling frequency—how many times per second the ECG signal is measured. If the sampling rate is too low, it can lead to a form of signal distortion called aliasing, where high-frequency signals masquerade as lower frequencies, contaminating the entire spectrum (Burma et al., 2021).

Furthermore, the specific mathematical algorithm used to perform the frequency-domain analysis—whether the classic Fast Fourier Transform (FFT), autoregressive models, or newer wavelet-based approaches—can yield different results in the higher frequencies, raising questions about the robustness of any observed VHF power (Tzabazis et al., 2018).

Third, many experts argue that much of the observed VHF activity is simply a harmonic. A harmonic is a frequency that is an integer multiple of a fundamental frequency, much like how a single vibrating guitar string produces a fundamental note along with quieter, higher-pitched overtones. 

In HRV, the powerful and periodic rhythm of breathing in the HF band can create mathematical echoes of itself at double or triple its frequency, which would fall squarely within the VHF range. In this view, VHF power is not a distinct physiological rhythm but an epiphenomenon of RSA (Hayano & Yuda, 2019).

Despite these compelling counterarguments, a growing body of evidence suggests that a purely artifactual interpretation is incomplete. Studies employing meticulous, high-fidelity recording techniques and advanced, noise-resistant processing algorithms have consistently identified distinct power in the VHF band, even after accounting for potential artifacts (Estévez-Báez et al., 2018).

The most compelling evidence for a genuine physiological signal comes from the analysis of heart rate fragmentation. This is a measure that quantifies the number of times the heart rate changes direction (from accelerating to decelerating, or vice versa) from one beat to the next.

A high degree of fragmentation indicates a pattern of sinoatrial instability that is mathematically and physiologically distinct from the smooth, wave-like pattern of RSA. This fragmentation contributes significantly to power in the VHF band and is thought to reflect the intrinsic, beat-to-beat dynamics of the heart's own pacemaker, separate from autonomic neural inputs (Costa et al., 2017). This suggests that at least a portion of the VHF signal originates from within the heart itself. The intrinsic ganglia images © 2012 Dr. Andrew Armour and the Institute of HeartMath.

Potential Mechanisms and Clinical Relevance of VHF HRV

Assuming a genuine physiological component of VHF-HRV exists, understanding its origin is key to unlocking its clinical potential. The leading hypothesis centers on the intrinsic, high-frequency fluctuations of the sinoatrial (SA) node. Located in the heart's right atrium, the SA node is not a single point but a complex, three-dimensional cluster of specialized pacemaker cells that fire electrical impulses to initiate each heartbeat. SA node graphic  © Alila Medical Media/Shutterstock.com.

The interactions and synchronization among these millions of cellular oscillators could theoretically generate subtle, high-frequency rhythms that are independent of nervous system input (Yamamoto et al., 1995).

If VHF power is indeed a marker of SA node health and stability, its clinical implications could be significant. Alterations in this band could serve as an early, noninvasive marker for sinus node dysfunction, a condition often called "sick sinus syndrome."

This disorder involves the SA node failing to generate heartbeats consistently, leading to symptoms like dizziness, fainting, and fatigue. A metric that could quantify the underlying instability of the SA node before severe symptoms develop would be invaluable. The link between VHF and heart rate fragmentation is particularly relevant here, as increased fragmentation is already associated with aging and adverse outcomes in patients with coronary artery disease (Costa et al., 2017).

Furthermore, some research suggests VHF power may be altered in patients with cardiac autonomic neuropathy (CAN), a serious complication of diseases like diabetes where the nerves that control the heart are damaged. Intriguingly, some studies have found that VHF power increases in these patients, even as their HF power (a marker of vagal health) declines. This has led to the hypothesis that the rise in VHF could reflect a pathological state of dysregulation within the SA node as it loses its primary autonomic control, or perhaps a failed compensatory mechanism (Estévez-Báez et al., 2018). This could offer a unique window into the progression of CAN that is not visible with standard HRV bands alone.

Conclusion: A Field Awaiting Consensus

The very-high-frequency band in HRV is a measurable phenomenon, but its physiological significance remains uncertain and is the subject of active debate.

The current body of evidence suggests that VHF power is a complex mixture. It is partly composed of genuine physiological signals, likely originating from the intrinsic dynamics of the sinoatrial node, but it is also heavily contaminated by methodological artifacts, measurement noise, and mathematical harmonics of lower-frequency rhythms.

At present, there is no clear scientific consensus that VHF represents a distinct physiological process that can be reliably interpreted.

Before this metric can be adopted for clinical or research use, rigorous standardization of measurement techniques is essential, including required sampling rates and best practices for artifact correction. Further mechanistic research is needed to definitively separate the physiological signal from the noise and validate its meaning in well-defined patient populations.

Therefore, clinicians should view VHF data as strictly investigational, recognizing it as a promising but unproven frontier in the ongoing effort to understand the complex language of the heart.

Key Takeaways

1. VHF-HRV is a controversial band (~0.4–0.9 Hz) that lies beyond the standard, well-understood HRV frequencies and whose meaning is heavily debated.

   
   

2. A primary challenge is that VHF power can be an artifact of technical issues like measurement noise (jitter), insufficient sampling frequency (aliasing), or a mathematical echo (harmonic) of lower-frequency bands like respiration.

   
   

3. The most promising evidence for a "real" signal points to intrinsic dynamics of the heart's sinoatrial (SA) node, the natural pacemaker, which can be quantified through measures like heart rate fragmentation.

   
   

4. Potential clinical relevance is significant, with possible applications as an early marker for SA node dysfunction (sick sinus syndrome) or as a novel biomarker for tracking cardiac autonomic neuropathy.

   
   

5. There is no scientific consensus on the meaning of VHF-HRV; it should be treated as an exploratory metric requiring extensive validation and standardization before it is ready for clinical decision-making.

   
   

Glossary

artifact: a spurious signal or distortion in data that arises from measurement or processing errors rather than true physiological activity.

autonomic nervous system (ANS): the body's automatic control system regulating involuntary functions like heart rate and digestion through its sympathetic and parasympathetic branches.

baroreflex: a rapid negative feedback loop that maintains blood pressure at a nearly constant level.⁷

cardiac autonomic neuropathy (CAN): damage to the autonomic nerves that control the heart, a common complication of diabetes that increases cardiovascular risk.

Fast Fourier Transform (FFT): a standard mathematical algorithm used to decompose a time-domain signal, like a series of heartbeats, into its underlying frequency components.

frequency-domain analysis: a method of analyzing signals based on their frequency content, revealing the power of different underlying rhythms.

harmonic: a frequency component that is an integer multiple of a fundamental frequency. In HRV, a harmonic of the respiratory frequency may appear in the VHF band.

heart rate fragmentation: a measure of the frequent beat-to-beat changes in the direction of heart rate (speeding up vs. slowing down), thought to reflect sinoatrial node instability.

heart rate variability (HRV): the physiological phenomenon of variation in the time interval between consecutive heartbeats, used as a proxy for autonomic nervous system activity.

high-frequency (HF) band: the HRV band from 0.15–0.4 Hz, which is a well-established marker of parasympathetic (vagal) activity, primarily driven by respiration.

Hilbert-Huang Transform (HHT): an adaptive signal processing technique used to analyze non-linear and non-stationary signals—those whose frequency changes over time. It breaks a complex signal down into its fundamental oscillatory components to reveal how its frequency and amplitude vary at every moment.

jitter: small, unwanted deviations in the timing of a signal's events, rather than errors in the signal's value. It's a timing error, not an amplitude error. For example, if a digital clock's tick is slightly irregular instead of perfectly periodic, that irregularity is jitter.

low-frequency (LF) band: the HRV band from 0.04–0.15 Hz, reflecting a mix of sympathetic and parasympathetic influences, largely related to blood pressure regulation.

measurement noise: the random, unpredictable error or static that gets added to any measurement, corrupting the true underlying signal. It can originate from environmental interference (like electrical hum) or the limitations of the measurement device itself.

respiratory sinus arrhythmia (RSA): the natural increase and decrease in heart rate that occurs with each breath, generated by the vagus nerve.

sampling frequency (sampling rate): the number of times per second a continuous, real-world signal is measured (or "sampled") to convert it into a digital format. Measured in Hertz (Hz), a higher sampling frequency captures more detail, much like a camera with a higher frame rate captures smoother motion.

sinoatrial (SA) node: the heart's natural pacemaker, a cluster of specialized cells in the right atrium that generates the electrical impulses for each heartbeat.

sinus node dysfunction (sick sinus syndrome): a condition where the heart's natural pacemaker, the sinoatrial (SA) node, doesn't work correctly. The SA node is a small cluster of specialized cells in the heart's right atrium that initiates each heartbeat by sending out a regular electrical signal. standardization of measurement techniques: the process of developing and implementing uniform, agreed-upon protocols for how a particular measurement is taken and analyzed. The goal is to ensure that measurements are consistent, reliable, and comparable, no matter who is performing them or where they're being done.

very-high-frequency (VHF) band: the controversial and less-established HRV band, typically defined as 0.4–0.9 Hz.

References

Berntson, G. G., Bigger, J. T., 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. PMID: 9401419

Burma, J., Lapointe, A., Soroush, A., Oni, I. K., Smirl, J., & Dunn, J. (2021). Insufficient sampling frequencies skew heart rate variability estimates: Implications for extracting heart rate metrics from neuroimaging and physiological data. Journal of Biomedical Informatics, 121, 103887. https://doi.org/10.1016/j.jbi.2021.103887

Chang, C.-C., Hsiao, T., & Hsu, H. (2014). Frequency range extension of spectral analysis of pulse rate variability based on Hilbert–Huang transform. Medical & Biological Engineering & Computing, 52(1), 75–83. https://doi.org/10.1007/s11517-013-1102-2

Costa, M. D., Kudaibergenova, Z., & Goldberger, A. L. (2017). Heart rate fragmentation: A new approach to the analysis of cardiac interbeat interval dynamics. Frontiers in Physiology, 8, 255. https://doi.org/10.3389/fphys.2017.00255

Ernst, G. (2017). Hidden signals—The history and methods of heart rate variability. Frontiers in Public Health, 5, 265. https://doi.org/10.3389/fpubh.2017.00265

Estévez-Báez, M., Machado, C., Montes-Brown, J., Jas-García, J., Leisman, G., Schiavi, A., Machado-García, A., Carricarte-Naranjo, C., & Carmeli, E. (2018). Very high frequency oscillations of heart rate variability in healthy humans and in patients with cardiovascular autonomic neuropathy. Advances in Experimental Medicine and Biology, 1072, 1–12. https://doi.org/10.1007/5584_2018_246

Hayano, J., & Yuda, E. (2019). Pitfalls of assessment of autonomic function by heart rate variability. Journal of Physiological Anthropology, 38(1), 3. https://doi.org/10.1186/s40101-019-0193-2

Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and Nnorms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258

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. PMID: 8598027

Tzabazis, A., Eisenried, A., Yeomans, D., & Hyatt, I. V. (2018). Wavelet analysis of heart rate variability: Impact of wavelet selection. Biomedical Signal Processing and Control, 40, 1–7. https://doi.org/10.1016/j.bspc.2017.09.012

Yamamoto, Y., Hughson, R. L., & Peterson, J. C. (1995). Non-linear and non-stationary dynamics of heart rate in humans. In P. M. Milligan (Ed.), Control Engineering in Applied Medicine. Pergamon Press.

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 has 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 helped to maintain BCIA's certification programs.

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