Why the study?
Paroxysmal atrial fibrillation presents with intermittent, self-terminating episodes that are difficult to diagnose once normal sinus rhythm resumes.
Does the SPAR method and machine learning accurately detect paroxysmal atrial fibrillation from normal sinus rhythm in equine electrocardiograms?
Does the SPAR method and machine learning accurately detect paroxysmal atrial fibrillation from normal sinus rhythm in equine electrocardiograms?
The SPAR method combined with machine learning can accurately detect a history of paroxysmal atrial fibrillation from short, normal sinus rhythm ECG strips in an equine model, suggesting potential future applications in human ECG monitoring.
Equine model supports ML detection of PAF history from NSR ECGs; leaves open human translation and validation.
Background: Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes. Objective: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machine learning classification. Methods: -nearest neighbors, linear support vector machine, and radial basis function kernel support vector machine). Imbalanced data were handled by upsampling the minority (PAF) class. A final subject classification was made by taking a majority vote over results from the 9 ECG strips. Results: Our final cross-validated classification for a subject gave an accuracy of 89.0%, sensitivity of 94.8%, specificity of 87.1%, and receiver operating characteristic area under the curve of 0.98, taking PAF as the positive class. Conclusion: The SPAR method and machine learning generated a final model with high sensitivity, suggesting that PAF can be discriminated from short equine ECG strips. This preliminary study indicated that SPAR analysis of human ECG could support patient monitoring, risk stratification, and clinical decision-making.
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Huang et al. (2022) studied this question.
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