FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sci Rep / Detection of exercising ectopic atrial and ventricular beats...
Scientific reports2026; doi: 10.1038/s41598-026-41281-0

Detection of exercising ectopic atrial and ventricular beats using non-linear analysis of clinically normal racehorse electrocardiograms at rest or low-intensity exercise.

Abstract: Cardiac arrhythmias are common in healthy athletic horses and may lead to poor athletic performance or exercise-associated sudden death. Early detection of high-risk horses is an important goal of cardiovascular diagnostics. We hypothesised that non-linear analysis of electrocardiogram disorderliness can be used to identify horses exhibiting intermittent ectopic atrial and ventricular heart rhythm abnormalities at exercise using brief, artifact-free recordings of normal sinus rhythm electrocardiograms collected at submaximal heart rates. In a convenience prospective cross-sectional study, ambulatory electrocardiograms were recorded using the Televet 100 or II devices from 110 Thoroughbred or Standardbred racehorses during routine training. Acceptable quality 60 s electrocardiogram strips with stable heart rate (20-120 beats per minute) were identified automatically. Disorderliness of the electrocardiograms was estimated using Lempel-Ziv'76 and '78, and Titchener complexity, and Shannon, sample, and approximate entropy algorithms. Numerical estimates obtained by these algorithms were corrected to the heart rate. For the optimal performance recordings of 60-100 beats per minute should be used, with Lempel-Ziv '76 complexity, R peak and ends of S and T peaks as fiducial points. The receiver operating curve analysis has demonstrated the area under curve of 0.86 for this combination, indicating acceptable differentiation between cases and controls.
© 2026. The Author(s).
Get Full Text
Publication Date: 2026-03-13 PubMed ID: 41826418DOI: 10.1038/s41598-026-41281-0Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigates whether non-linear analysis of electrocardiogram (ECG) signal complexity can detect abnormal heartbeats in racehorses during low-intensity exercise or rest.
  • The goal is to identify horses at risk of cardiac arrhythmias, which could impair performance or cause sudden death, by analyzing brief ECG recordings taken during routine training.

Background and Importance

  • Cardiac arrhythmias—irregular heartbeats—are commonly found even in healthy, athletic horses.
  • Such arrhythmias can reduce athletic performance and increase the risk of sudden cardiac death during exercise.
  • Detecting horses with potentially dangerous irregular heart rhythms early on is important for veterinary care and racehorse management.
  • Traditional detection often requires long or high-intensity exercise ECG monitoring, which can be difficult to obtain consistently.

Research Hypothesis

  • The authors hypothesized that non-linear methods of analyzing the disorderliness (complexity) of ECG signals could reveal subtle intermittent ectopic atrial and ventricular beats.
  • These ectopic beats are abnormal heartbeats arising from areas outside the normal heart rhythm pacemaker.
  • The approach could use short, clean ECG segments obtained during normal sinus rhythm at low to moderate heart rates without needing intense exercise conditions.

Study Design and Methods

  • 110 Thoroughbred or Standardbred racehorses undergoing routine training were selected for a cross-sectional study.
  • Ambulatory ECGs were recorded using Televet 100 or Televet II devices.
  • From these recordings, 60-second ECG strips were automatically selected if they met criteria for acceptable signal quality and stable heart rates between 20 and 120 beats per minute.
  • The study focused particularly on recordings where heart rates ranged from 60 to 100 beats per minute for optimal analysis performance.

Analytical Techniques

  • Electrocardiogram disorderliness was measured via multiple non-linear complexity algorithms:
    • Lempel-Ziv complexity algorithms (‘76 and ‘78 versions)
    • Titchener complexity
    • Shannon entropy
    • Sample entropy
    • Approximate entropy
  • These algorithms quantify different aspects of signal irregularity and complexity, which can correlate with arrhythmias.
  • Numerical values from these methods were corrected for differences in heart rate to control confounding effects.
  • Electrocardiogram fiducial points (reference markers) selected were the R peak as well as ends of S and T waves, which are key features of the heartbeat waveform.

Results and Interpretation

  • The best performing model used the Lempel-Ziv ’76 complexity algorithm focusing on recordings with heart rates between 60 and 100 bpm.
  • This method successfully differentiated between horses with and without intermittent ectopic beats.
  • Receiver Operating Characteristic (ROC) curve analysis was used to assess diagnostic accuracy.
  • The area under the ROC curve (AUC) was 0.86, indicating strong ability to distinguish arrhythmic cases from controls with a good balance of sensitivity and specificity.
  • An AUC of 0.86 is considered indicative of acceptable to excellent diagnostic performance in clinical studies.

Conclusions and Implications

  • Non-linear complexity analysis of short, artifact-free ECG segments recorded at rest or low-intensity exercise can identify racehorses with intermittent atrial and ventricular ectopic beats.
  • This method provides a non-invasive and practical diagnostic tool that does not require prolonged or intense exercise testing.
  • Early detection using these techniques may enable veterinarians and trainers to monitor and manage at-risk horses more effectively.
  • Further research may be warranted to validate this approach across broader populations and different levels of exercise intensity.

Cite This Article

APA
Alexeenko V, Tavanaeimanesh H, Stein F, Gold J, Hughes L, McCue M, Marr C, Durward-Akhurst S, Jeevaratnam K. (2026). Detection of exercising ectopic atrial and ventricular beats using non-linear analysis of clinically normal racehorse electrocardiograms at rest or low-intensity exercise. Sci Rep. https://doi.org/10.1038/s41598-026-41281-0

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English

Researcher Affiliations

Alexeenko, Vadim
  • University of Surrey, Guildford, UK.
Tavanaeimanesh, Hamid
  • Department of Veterinary Clinical Sciences, University of Minnesota, Minnesota, USA.
Stein, Freya
  • Department of Veterinary Clinical Sciences, University of Minnesota, Minnesota, USA.
Gold, Jenifer
  • Wisconsin Equine Clinic, Oconomowoc, Wisconsin, USA.
Hughes, Lauren
  • Department of Veterinary Population Medicine, University of Minnesota, Minnesota, USA.
McCue, Molly
  • Department of Veterinary Population Medicine, University of Minnesota, Minnesota, USA.
Marr, Celia
  • Rossdales Veterinary Surgeons, Newmarket, UK.
Durward-Akhurst, Sian
  • Department of Veterinary Clinical Sciences, University of Minnesota, Minnesota, USA. durwa004@umn.edu.
Jeevaratnam, Kamalan
  • University of Surrey, Guildford, UK. k.jeevaratnam@surrey.ac.uk.

Grant Funding

  • K12TR002492 and UL1TR002494 / National Institutes of Health's National Center for Advancing Translational Sciences

Conflict of Interest Statement

Declarations. Competing interests: The authors declare no competing interests.

References

This article includes 28 references
  1. Buhl R, Petersen EE, Lindholm M, Bak L, Nostell K. Cardiac arrhythmias in Standardbreds during and after racing—possible association between heart size, valvular regurgitations, and arrhythmias.. J. Equine Vet. Sci. 33, 590–596 (2013).
  2. Massie SL, Bezugley RJ, McDonald KJ, Leguillette R. Prevalence of cardiac arrhythmias and R-R interval variation in healthy Thoroughbred horses during official Chuckwagon races and recovery.. Vet. J. 267, 105583 (2021).
  3. Reef VB et al. Recommendations for management of equine athletes with cardiovascular abnormalities.. J. Vet. Intern. Med. 28, 749–761 (2014).
  4. Bennet ED, Parkin TDH. Fifteen risk factors associated with sudden death in Thoroughbred racehorses in North America (2009-2021).. J. Am. Vet. Med. Assoc. 260, 1956–1962 (2022).
  5. Alexeenko V et al. Prediction of paroxysmal atrial fibrillation from complexity analysis of the sinus rhythm ECG: A retrospective case/control pilot study.. Front. Physiol. 12, 570705 (2021).
  6. Alexeenko V et al. The complexity of clinically-normal sinus-rhythm ECGs is decreased in equine athletes with a diagnosis of paroxysmal atrial fibrillation.. Sci. Rep. 10, 1–10 (2020).
  7. Huang YH et al. ECG restitution analysis and machine learning to detect paroxysmal atrial fibrillation: Insight from the equine athlete as a model for human athletes.. Function 2, 1–10 (2020).
  8. Alexeenko V et al. The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings.. Sci. Rep. 9, 2619 (2019).
  9. Goldberger A. Is the normal heartbeat chaotic or homeostatic?. Physiology 6, 87–91 (1991).
  10. Fossa AA. Beat-to-beat ECG restitution: A review and proposal for a new biomarker to assess cardiac stress and ventricular tachyarrhythmia vulnerability.. Ann. Noninvasive Electrocardiol. 22, 1–11 (2017).
  11. Li M et al. Cardiac electrophysiological adaptations in the equine athlete-Restitution analysis of electrocardiographic features.. PLoS ONE 13, e0194008 (2018).
  12. Creasy S et al. Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms.. Heart Rhythm O2 6, 48–57 (2025).
  13. Lempel A, Ziv J. Complexity of finite sequences.. IEEE Trans. Inf. Theory 22, 75–81 (1976).
  14. Titchener MR. Deterministic computation of complexity, information and entropy.. In: Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252) 22 326 (IEEE, 1998).
  15. Shannon CE. A mathematical theory of communication.. Bell Syst. Tech. J. 27, 379–423 (1948).
  16. Pincus, S. M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88, 2297–2301 (1991).
  17. Richman, J. S. & Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000).
  18. Wyse, D. G. et al. Lone atrial fibrillation: does it exist?. J. Am. Coll. Cardiol. 63, 1715–1723 (2014).
  19. Pedersen, P. J., Kanters, J. K., Buhl, R. & Klaerke, D. A. Normal electrocardiographic QT interval in race-fit Standardbred horses at rest and its rate dependence during exercise. J. Vet. Cardiol. 15, 23–31 (2013).
  20. Johnson, N. P., Holly, T. A. & Goldberger, J. J. QT dynamics early after exercise as a predictor of mortality. Heart Rhythm 7, 1077–1084 (2010).
  21. Speidel, U. A note on the estimation of string complexity for short strings. In: 7th International Conference on Information, Communications and Signal Processing (ICICS) 1–5 https://doi.org/10.1109/ICICS.2009.5397536. (IEEE, 2009).
  22. Yentes, J. M. et al. The appropriate use of approximate entropy and sample entropy with short data sets. Ann. Biomed. Eng. 41, 349–365 (2013).
  23. Lesne, A., Blanc, J. L. & Pezard, L. Entropy estimation of very short symbolic sequences. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 79, 1–10 (2009).
  24. Diederichsen, S. Z. et al. Comprehensive evaluation of rhythm monitoring strategies in screening for atrial fibrillation: insights from patients at risk monitored long term with an implantable loop recorder. Circulation 141, 1510–1522 (2020).
  25. Ziv, J. & Lempel, A. Compression of individual sequences via variable-rate coding. IEEE Trans. Inf. Theory 24, 530–536 (1978).
  26. Kaspar, F. & Schuster, H. G. Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A (Coll. Park) 36, 842–848 (1987).
  27. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2026).
  28. Mitchell, A. J. Sensitivity × PPV is a recognized test called the clinical utility index (CUI+). Eur. J. Epidemiol. 26, 251–252 (2011).

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,001 horse owners like you who receive our email updates on horse health and nutrition.