Key result
Heart rate variability analysis, enhanced by machine learning and complex algorithms, is increasingly utilized for the diagnosis of cardiac arrhythmias like atrial fibrillation in equine cardiology.
Why the study?
Heart rate variability analysis techniques using algorithms, neural networks, and machine learning are expanding and being translated into equine cardiology to improve arrhythmia diagnosis.
Heart rate variability analysis, incorporating machine learning and complex algorithms, is being adapted for equine cardiology to improve arrhythmia diagnosis.
Rapid tech-driven HRV evolution may aid arrhythmia detection; leaves open prospective validation in veterinary patients.
Heart rate variability (HRV) analysis has been performed on ECG-derived data sets for more than 170 years but is currently undergoing a rapid evolution, thanks to the expansion of the human and veterinary medical technology sector. Traditional HRV analysis was initially performed to identify changes in vago-sympathetic balance, while the most recent focus has expanded to include the use of complex computer algorithms, neural networks and machine learning technology to identify cardiac arrhythmias, particularly atrial fibrillation (AF). Some of these techniques have recently been translated for use in the field of equine cardiology, with particular focus on improving the diagnosis of arrhythmias both at rest and during exercise. This review focuses on understanding the basic HRV variables and important factors to consider when collecting data for use in HRV analysis. In addition, the use of HRV analysis for the diagnosis of arrhythmias is discussed from human, small animal and equine perspectives. Finally, the future of HRV analysis is briefly introduced, including an overview of future developments in this rapidly expanding and exciting field.
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Mitchell et al. (2020) conducted a review in Cardiac arrhythmias (particularly atrial fibrillation). Heart rate variability (HRV) analysis was evaluated. Heart rate variability analysis, enhanced by machine learning and complex algorithms, is increasingly utilized for the diagnosis of cardiac arrhythmias like atrial fibrillation in equine cardiology.
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