Heart rate variability analysis in horses for the diagnosis of arrhythmias.
Authors: Mitchell Katharyn J, Schwarzwald Colin C
Journal: Veterinary journal (London, England : 1997)
Summary
# Editorial Summary: Heart rate variability analysis in horses for the diagnosis of arrhythmias Mitchell Katharyn J and Colin C Schwarzwald's 2021 review examines how heart rate variability (HRV) analysis—a technique with over 170 years of precedent in ECG interpretation—is evolving as a diagnostic tool for equine cardiac arrhythmias, particularly atrial fibrillation. Whilst traditional HRV assessment focused on identifying shifts in autonomic nervous system balance, contemporary approaches increasingly employ machine learning and advanced algorithms to detect arrhythmias in both resting and exercising horses. The authors synthesise evidence from human, small animal and equine contexts to clarify the fundamental HRV variables essential for robust data collection and interpretation, establishing practical parameters for clinicians and researchers working with equine patients. For equine practitioners, understanding which HRV metrics are reliable, how field conditions influence measurement quality, and where the technology currently succeeds or falls short is critical when integrating these tools into diagnostic protocols—particularly for performance horses where occult arrhythmias may compromise athletic function. As portable electrocardiographic devices and wearable monitoring systems become increasingly sophisticated, this evidence-based overview positions HRV analysis as a potentially valuable adjunct to traditional auscultation and resting ECG, though practitioners should remain cognisant of the technology's current limitations and the ongoing development of validated algorithms specific to equine populations.
Read the full abstract on PubMed
Practical Takeaways
- •HRV analysis offers a non-invasive diagnostic tool to help identify cardiac arrhythmias in horses, potentially improving early detection of conditions like atrial fibrillation
- •Proper ECG data collection protocols and understanding of HRV measurement variables are essential for reliable results in clinical practice
- •Advanced HRV analysis using machine learning may enhance diagnostic accuracy for equine arrhythmias, particularly during exercise when some arrhythmias are more apparent
Key Findings
- •Heart rate variability analysis has evolved from identifying vago-sympathetic balance changes to using machine learning algorithms for arrhythmia diagnosis
- •HRV analysis techniques developed in human and small animal medicine are being translated for equine cardiology applications
- •HRV analysis shows promise for diagnosing arrhythmias in horses both at rest and during exercise
- •Understanding basic HRV variables and proper data collection methodology are critical factors for accurate arrhythmia detection