The complexity of clinically-normal sinus-rhythm ECGs is decreased in equine athletes with a diagnosis of paroxysmal atrial fibrillation

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Paroxysmal Atrial Fibrillation in Horses: Pathophysiology, Diagnostics and Clinical Aspects

Atrial fibrillation (AF) is the most common arrhythmia in horses causing poor performance. As in humans, the condition can be intermittent in nature, known as paroxysmal atrial fibrillation (pAF). This review covers the literature relating to pAF in horses and includes references to the human literature to compare pathophysiology, clinical presentation, diagnostic tools and treatment. The arrhythmia is diagnosed by auscultation and electrocardiography (ECG), and clinical signs can vary from sudden loss of racing performance to reduced fitness or no signs at all. If left untreated, pAF may promote electrical, functional and structural remodeling of the myocardium, thus creating a substrate that is able to maintain the arrhythmia, which over time may progress into permanent AF. Long-term ECG monitoring is essential for diagnosing the condition and fully understanding the duration and frequency of pAF episodes. The potential to adapt human cardiac monitoring systems and computational ECG analysis is therefore of interest and may benefit future diagnostic tools in equine medicine.

Fundamentals of arrhythmogenic mechanisms and treatment strategies for equine atrial fibrillation

Atrial fibrillation (AF) is the most common pathological arrhythmia in horses. Although it is not usually a life-threatening condition on its own, it can cause poor performance and make the horse unsafe to ride. It is a complex multifactorial disease influenced by both genetic and environmental factors including exercise training, comorbidities or ageing. The interactions between all these factors in horses are still not completely understood and the pathophysiology of AF remains poorly defined. Exciting progress has been recently made in equine cardiac electrophysiology in terms of diagnosis and documentation methods such as cardiac mapping, implantable electrocardiogram (ECG) recording devices or computer-based ECG analysis that will hopefully improve our understanding of this disease. The available pharmaceutical and electrophysiological treatments have good efficacy and lead to a good prognosis for AF, but recurrence is a frequent issue that veterinarians have to face. This review aims to summarise our current understanding of equine cardiac electrophysiology and pathophysiology of equine AF while providing an overview of the mechanism of action for currently available treatments for equine AF.

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.

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.

Implantable loop recorders can detect paroxysmal atrial fibrillation in Standardbred racehorses with intermittent poor performance

Background

Limited information is available on paroxysmal atrial fibrillation (PAF) in the horse. Indeed, undiagnosed PAF could result in poor performance. Due to the intermittent occurrence, PAF is difficult to diagnose. However, implanting a small ECG device (implantable loop recorder, ILR) subcutaneously, allows the continuous and automatic detection of PAF.

Objectives

The aim was to investigate the potential of ILRs as a tool for diagnosing PAF in horses with poor performance.

Study design

Prospective field study.

Methods

Twelve racing Standardbred trotters with intermittent reduced performance (mean age: six years) were enrolled prospectively. The ILR was implanted subcutaneously at the fifth or sixth left intercostal space and data from the ILR was collected during the study period in which the horses were followed for a median duration of 7.5 month (range 6‐28).

Results

The ILR was able to detect PAF in four out of twelve racehorses. The ILR also detected sustained atrial fibrillation (AF) in one horse during the study. The ILRs rely on RR detection and R waves were correctly identified in 96%. One hundred episodes were categorised as AF by the ILRs and subsequently visual ECG inspection categorised 12 as sinus rhythm (SR), 28 as sinus arrhythmia (SA), 14 as other arrhythmias and 46 as AF episodes. The Root Mean Square of the Successive Differences (RMSSD) values were significantly increased for AF compared to SR and SA.

Main limitations

Few horses included and duration of study period varied among the horses. Further it was not possible to assess the sensitivity of the device in the current study and the ILRs proved to have a high rate of false positive misclassifications.

Conclusions

This study indicates that ILRs can be used for detection of PAF episodes and could be a useful ECG tool for horses presenting with poor performance. This methodology provides a platform to facilitate the long‐term assessment of AF development and quantification of AF burden in horses. Further studies including both healthy and poor performing horses are needed in order to learn more about PAF prevalence in racehorses.

ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes

Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3-41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.