Instantaneous and averaged heart rate profiles: Developing strategies for programming pacing rates in dogs.

Pacemakers use heart rate histograms (% beats) and sensor indicated rate histograms (% time) to illustrate rate distributions. When programmed to the rate adaptive modes, these data are used to determine the appropriateness of rate response to activity. These histograms are generated from instantaneous heart rate calculations. In humans, such data are compared to known histographic rate profiles. Such rate profiles during 24 h in the dog are not available. Moreover, data representation differ between Holter monitoring and pacemakers making comparisons challenging. The rate distribution in dogs >7-years of age was determined over 24 h using instantaneous and rolling average heart rate. Such data could serve as a guide to programming pacing rates for dogs. Sinus arrhythmia resulted in dissimilar heart rate profiles depending on the method of determining rate. The long intervals of sinus arrhythmia resulted in median values for the percent of time with an instantaneous heart rate of <50 beats/min (bpm) of 15%, whereas a rolling average heart rate of <50 bpm was 0.2%. Based on the cumulative time of the rolling average rate, dogs spent 26.3% of the day between 70-90 bpm with rates <65 bpm and >90 bpm approximating 30% for each. Rates >160 bpm were uncommon (<1%). However, high variability existed between dogs. This study demonstrated the shortcomings of both instantaneous and averaging methods to evaluate heart rate profiles in the dog and that both methods should be incorporated when making pacing rate decisions during programming.

Heart rate variability analysis in horses for the diagnosis of arrhythmias.

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.

Can heart rate variability parameters derived by a heart rate monitor differentiate between atrial fibrillation and sinus rhythm?

BACKGROUND: Heart rate variability (HRV) parameters, and especially RMSSD (root mean squared successive differences in RR interval), could distinguish atrial fibrillation (AF) from sinus rhythm(SR) in horses, as was demonstrated in a previous study. If heart rate monitors (HRM) automatically calculating RMSSD could also distinguish AF from SR, they would be useful for the monitoring of AF recurrence. The objective of the study was to assess whether RMSSD values obtained from a HRM can differentiate AF from SR in horses. Furthermore, the impact of artifact correction algorithms, integrated in the analyses software for HRV analyses was evaluated. Fourteen horses presented for AF treatment were simultaneously equipped with a HRM and an electrocardiogram (ECG). A two-minute recording at rest, walk and trot, before and after cardioversion, was obtained. RR intervals used were those determined automatically by the HRM and by the equine ECG analysis software, and those obtained after manual correction of QRS detection within the ECG software. RMSSD was calculated by the HRM software and by dedicated HRV software, using six different artifact filters. Statistical analysis was performed using the Wilcoxon signed-rank test and receiver operating curves. RESULTS: The HRM, which applies a low level filter, produced high area under the curve (AUC) (> 0.9) and cut off values with high sensitivity and specificity. Similar results were obtained for the ECG, when low level artifact filtering was applied. When no artifact correction was used during trotting, an important decrease in AUC (0.75) occurred. CONCLUSION: In horses treated for AF, HRMs with automatic RMSSD calculations distinguish between AF and SR. Such devices might be a useful aid to monitor for AF recurrence in horses.

Heart rate variability in relation to stress in the Asian elephant (Elephas maximus).

This study describes a safe, reliable, and accessible means to measure heart rate (HR) and HR variability (HRV) and evaluates the use of HRV as a physiological correlate of stress in the Asian elephant. A probabilistic model indicates that HRV measurements may adequately distinguish between stressed and non-stressed elephants.

Variabilité de la fréquence cardiaque en relation avec le stress chez un éléphant d'Asie(Elephas maximus). Cette étude décrit un moyen sûr, fiable et accessible de mesurer la fréquence cardiaque (FC) et la variabilité de la FC (VFC) et évalue l'utilisation de la VFC comme corrélat physiologique du stress chez l'éléphant d'Asie. Un modèle probabiliste indique que les mesures de la VFC peuvent adéquatement distinguer entre des éléphants stressés et non stressés.(Traduit par Isabelle Vallières).