No abstract

Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via 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 was trained and tested using k-nearest neighbour (k-NN) algorithm with various values of neighbours k to derive a discrimination tool. A combination of QT, RR and TQ intervals was used to analyse 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 paroxysmal atrial fibrillation 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 AUCs of 0.9 were obtained for all tested k values (3 to 41). We concluded that three-dimensional ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.

equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv ('76 & '78) and Titchener complexity estimators. Dependence of Lempel-Ziv '76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25-60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.www.nature.com/scientificreports www.nature.com/scientificreports/ Complexity analysis. Estimation of the binary string complexities was facilitated by a custom implementation of a complexity evaluator developed in C++ for the Linux operating system. The program simultaneously performs complexity analysis using three previously published methods: Lempel-Ziv '76 24 , Lempel-Ziv '78 28 and Titchener T-complexity 31 . All three methods estimate the complexity of a symbolic string by identifying the number of sub-strings (factors) needed to build it by a computer capable of a certain limited set of operations. These methods differ by the algorithms of decomposition of the source strings to sub-strings and therefore produce different estimates for the same source data (Fig. 5). The detailed description of these methods may be found in the corresponding publications. To eliminate the dependency of complexity values on the length of the source string (n), Lempel-Ziv '76 (abbreviated as LZ76) complexity values were normalised to the n/log 2 (n) value 25 . Lempel-Ziv '78 (abbreviated as LZ78) values were normalised to sequence length. For T-complexity, average entropy values were used.Statistical analyses. Parametric data are expressed as mean ± standard deviation of mean. Statistical analyses and plotting were done using GNU R 40 . An unpaired two-sided t-test (using Welch's correction for unequal variances) was used for two-group comparisons and ANOVA test with Tukey correction for multiple comparisons of groups, with significance between data sets accepted at p < 0.05.