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Function (Oxford, England)2020; 2(1); zqaa031; doi: 10.1093/function/zqaa031

ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes.

Abstract: Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3-41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.
© The Author(s) 2020. Published by Oxford University Press on behalf of American Physiological Society.
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Publication Date: 2020-11-18 PubMed ID: 35330977PubMed Central: PMC8788737DOI: 10.1093/function/zqaa031Google Scholar: Lookup
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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.

This research paper investigates a new method of detecting paroxysmal atrial fibrillation (PAF) in horses and potentially humans by using ECG restitution analysis and machine learning. The researchers found that this methodology predicted PAF with high accuracy, indicating its potential as an alternative screening tool to standard electrocardiogram (ECG) monitoring.

Research Methodology & Data Collection

  • The researchers gathered ECG recordings from horses during routine clinical practices. These horses were either controls or afflicted with PAF.
  • The ECG recordings were then analyzed for patterns in the QT, TQ, and RR intervals – elements that are essential in diagnosing conditions related to heart rhythm.
  • Data from this analysis, known as ECG restitution analysis, were used for training a machine learning model.

Machine Learning Analysis

  • A k-nearest neighbor (k-NN) algorithm was used for the machine learning model. This model was trained and tested on the restitution data, with different values of neighbors used in an experiment to optimize the model’s performance.
  • The purpose of this model was to develop a tool that could distinguish between horses with and without PAF, based purely on the extracted ECG restitution data.

Results of the Analysis

  • The relationship between the QT, RR, and TQ intervals and their impact on PAF was analyzed.
  • The k-NN classifiers, when using two-interval measures, were found to predict PAF diagnosis with high accuracy – an area under the receiving operating characteristic curve close to 0.8 and 0.9.
  • Even better results were obtained when using all three interval measures simultaneously, and by using a majority voting system for final beat classification; in this case, the mean area under the curve was 0.9, indicative of a high level of diagnostic accuracy.

Conclusion

  • The study concluded that 3D ECG restitution analysis, paired with machine learning algorithms, could be used as a potential method for PAF screening.
  • The high level of accuracy suggests its viability as an automated method that may provide more precise detection in comparison to conventional ECG monitoring.
  • While the study was conducted on equine subjects, the findings could hold potential applicability for human athletes as well.

Cite This Article

APA
Huang YH, Alexeenko V, Tse G, Huang CL, Marr CM, Jeevaratnam K. (2020). ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes. Function (Oxf), 2(1), zqaa031. https://doi.org/10.1093/function/zqaa031

Publication

Function (Oxford, England)
ISSN: 2633-8823
NlmUniqueID: 101770668
Country: England
Language: English
Volume: 2
Issue: 1
Pages: zqaa031

Researcher Affiliations

Huang, Ying H
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK.
Alexeenko, Vadim
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK.
Tse, Gary
  • Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China.
Huang, Christopher L-H
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK.
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 1QW, UK.
Marr, Celia M
  • Rossdales Equine Hospital and Diagnostic Centre, Exning, CB8 7NN, Suffolk, UK.
Jeevaratnam, Kamalan
  • Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK.
  • Physiological Laboratory, University of Cambridge, Cambridge, CB2 1QW, UK.

MeSH Terms

  • Humans
  • Horses
  • Animals
  • Atrial Fibrillation / diagnosis
  • Electrocardiography / methods
  • Heart Rate
  • Machine Learning

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Citations

This article has been cited 9 times.
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