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Home / Equine Research Bank / Sci Rep / The application of Lempel-Ziv and Titchener complexity analy...
Scientific reports2019; 9(1); 2619; doi: 10.1038/s41598-019-38935-7

The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings.

Abstract: The analysis of equine electrocardiographic (ECG) recordings is complicated by the absence of agreed abnormality classification criteria. We explore the applicability of several complexity analysis methods for characterization of non-linear aspects of electrocardiographic recordings. We here show that complexity estimates provided by Lempel-Ziv '76, Titchener's T-complexity and Lempel-Ziv '78 analysis of ECG recordings of healthy Thoroughbred horses are highly dependent on the duration of analysed ECG fragments and the heart rate. The results provide a methodological basis and a feasible reference point for the complexity analysis of equine telemetric ECG recordings that might be applied to automate detection of equine arrhythmias in equine clinical practice.
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Publication Date: 2019-02-22 PubMed ID: 30796330PubMed Central: PMC6385502DOI: 10.1038/s41598-019-38935-7Google Scholar: Lookup
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  • Non-U.S. Gov't

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.

The research explores the use of complexity analysis methods to identify non-linear properties in the ECG readings of healthy Thoroughbred horses, and examines the influence of fragment duration and heart rate on these estimates. The findings offer a methodology for the complexity analysis of equine ECG recordings that could help automate the detection of equine arrhythmias.

Objective of the Research

  • The research was motivated by the lack of a consensus on what constitutes abnormality in equine electrocardiographic (ECG) recordings. Its main objective was to evaluate the utility of complexity analysis methods for characterizing the non-linear aspects of such recordings. The complexity analysis methods deployed in this study were Lempel-Ziv ’76, Titchener’s T-complexity and Lempel-Ziv ’78.

Methodology

  • The study used ECG recordings of healthy Thoroughbred horses as its primary data source. The fragment duration and the heart rate were chosen as the variables for the complexity estimates generated by the three aforementioned analysis methods.

Findings

  • The researchers found that the complexity estimates derived from the three analysis methods depended highly on the duration of the ECG fragments analysed and the heart rate. It implies that these factors should be taken into consideration while using these methods for complexity analysis of ECG readings.

Implications

  • A crucial outcome of the study is the establishment of a methodological foundation for the complexity analysis of equine ECG recordings, particularly those monitored through telemetry. This could be potentially useful for clinical practice with automated detection of equine arrhythmias, that is, irregular heart rhythms, becoming a possibility.

Conclusion

  • By exploring the applications of complexity analysis methods for equine ECG recordings, the research illuminates a possible pathway for advancing diagnosis in veterinary cardiology. It highlights the important variables that must be considered when applying these methods, and provides a feasible reference point for future work in this area.

Cite This Article

APA
Alexeenko V, Fraser JA, Dolgoborodov A, Bowen M, Huang CL, Marr CM, Jeevaratnam K. (2019). The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings. Sci Rep, 9(1), 2619. https://doi.org/10.1038/s41598-019-38935-7

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 9
Issue: 1
Pages: 2619
PII: 2619

Researcher Affiliations

Alexeenko, Vadim
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom.
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.
Fraser, James A
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.
Dolgoborodov, Alexey
  • Seven Bridge Genomics, Boston, MA, 02142, USA.
Bowen, Mark
  • Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, NG7 2UH, United Kingdom.
Huang, Christopher L-H
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.
  • Division of Cardiovascular Biology, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, United Kingdom.
Marr, Celia M
  • Rossdales Equine Hospital and Diagnostic Centre, Exning, CB8 7NN, Suffolk, United Kingdom.
Jeevaratnam, Kamalan
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom. drkamalanjeeva@gmail.com.
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom. drkamalanjeeva@gmail.com.

MeSH Terms

  • Algorithms
  • Animals
  • Electrocardiography
  • Heart Rate / physiology
  • Horses / physiology
  • Signal Processing, Computer-Assisted
  • Systems Analysis
  • Telemetry

Conflict of Interest Statement

The authors declare no competing interests.

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Citations

This article has been cited 3 times.
  1. Huang YH, Lyle JV, Ab Razak AS, Nandi M, Marr CM, Huang CL, Aston PJ, Jeevaratnam K. Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning.. Cardiovasc Digit Health J 2022 Apr;3(2):96-106.
    doi: 10.1016/j.cvdhj.2022.02.001pubmed: 35493267google scholar: lookup
  2. Huang YH, Alexeenko V, Tse G, Huang CL, Marr CM, Jeevaratnam K. ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes.. Function (Oxf) 2021;2(1):zqaa031.
    doi: 10.1093/function/zqaa031pubmed: 35330977google scholar: lookup
  3. Alexeenko V, Howlett PJ, Fraser JA, Abasolo D, Han TS, Fluck DS, Fry CH, Jabr RI. Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study.. Front Physiol 2021;12:570705.
    doi: 10.3389/fphys.2021.570705pubmed: 33679427google scholar: lookup
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