Cardiac arrhythmias are an important cause of poor performance and sudden cardiac death in racehorses. Atrial fibrillation (AF), is the most common clinically significant arrhythmia in racehorses, with a career prevalence of up to 4.9% in Thoroughbreds. Episodes are often transient and undetected, limiting early intervention. Premature depolarisations during exercise are identified more commonly (up to 92% during racing). However, their clinical significance currently remains uncertain, necessitating further investigation. Traditional monitoring systems are limited to controlled environments, limiting their usability for routine surveillance during training.
Wearable technology and smart textile ECG systems now enable continuous, field-based cardiac monitoring, offering a transformative approach to early arrhythmia detection. This review evaluated the current landscape of ECG monitoring technologies during high-speed exercise, drawing on peer-reviewed studies, manufacturer specifications, and validation trials. Key assessment criteria included signal quality, motion artefact mitigation, user comfort, multi-parameter integration, and capabilities for longitudinal monitoring.
Smart textile ECG systems, which embed electrodes into wearable surcingles, have been shown to produce recordings comparable to reference veterinary devices while reducing motion artefact and eliminating the need for adhesive electrodes. Fitness monitors, further enhance monitoring by integrating cardiac data with stride length, stride frequency, speed, and recovery metrics, providing context for arrhythmia interpretation and additional performance information for trainers. Longitudinal, cloud-based monitoring enables early detection of abnormal trends and benchmarking across training populations.
Although commercially available systems remain limited, integrating wearable ECG technology with multi-parameter monitoring shifts arrhythmia detection from reactive to continuous surveillance. Looking forward, artificial intelligence has the potential to automate and enhance ECG analysis, improving sensitivity for subtle arrhythmias and enabling real-time, welfare-focused decision making. This approach supports evidence-based training, recovery, and race management, and has the potential to improve equine welfare by identifying “at-risk” horses before catastrophic events occur.