Characterization of rate and regularity of ventricular response during atrial tachyarrhythmias. Insight on atrial and nodal determinants

Masè, Michela; Disertori, Marcello; Marini, Massimiliano; Ravelli, Flavia

doi:10.1088/1361-6579/aa6388

Cited by 13 publications

(6 citation statements)

References 53 publications

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“…This disorganized activation causes ineffective atrial contractions, such that the common P-waves in SR are replaced by irregular fibrillatory ( -) waves during AF [21]. Moreover, the atrioventricular (AV) node also transmits during the arrhythmia impulses to the ventricles more irregularly and quickly than for SR [22]. Because ventricular contractions are reflected on the ECG as QRS complexes, the series of temporal distances between R-peaks is then characterized to be quick and irregular during AF.…”

Section: Introductionmentioning

confidence: 99%

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

et al. 2017

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Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram (ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.

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Section: Introductionmentioning

confidence: 99%

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

et al. 2017

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“…2d) present deformations compared to healthy P wave (Fig. 2c), a consequence of the arrhythmia's associated with atrium (e.g., atrial fibrillation, atrial flutter) [29][30][31][32]. Ventricular diseases, such as heart block, cause irregularities in the electrical activity of the ventricles, which is evident in QRS complexes with arrhythmia (Fig.…”

Section: Discussionmentioning

confidence: 95%

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

Queiroz

Azoubel

Barros

2019

EURASIP J. Adv. Signal Process.

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Background: Several authors use the R-R interval, which is the temporal difference between the largest waves (R waves) of the electrocardiogram (ECG), to propose a support system for the diagnosis of arrhythmias. However, R-R interval analysis does not measure ECG waveform deformations such as P wave deformations for atrial fibrillation. Objective: In this study, we propose an arbitrary analysis the any segment of the heartbeat. This analysis is a generalization of a previous work that measures the wave deformations of the ECG signal. Methods: We proposed to investigate the voltage (mV) variation occurring at each heartbeat interval using statistical moments. Unlike the R-R interval in which each heartbeat is associated with a single real number, the proposed method associates each heartbeat to a set of points, that is, a vector. The heartbeats were obtained in the following databases: MIT-BIH Normal Sinus Rhythm, MIT-BIH Atrial Fibrillation (AF), and MIT-BIH Arrhythmia; and the classifiers used to evaluate the proposed method were linear discriminant analysis, k-nearest neighbors, and support vector machine. The experiments were conducted using 80% of the patients for training (16 healthy patients, 41 patients with arrhythmia, and 20 patients with AF) and 20% of the patients for testing (2 healthy patients, 6 patients with arrhythmia, and 3 patients with AF). Results: The proposed method proved to be efficient in solving global (accuracy is up to 99.78% in the arrhythmia classification) and local (accuracy of 100% in the AF classification) heartbeat problems. Conclusion: The results obtained by the proposed method can be used to support decision-making in clinical practices.

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“…There have been a number of studies that focused mainly on the regularity of AF [ 42 , 43 , 44 ]. Another popular group of methods used in AF electrogram complexity assessment are entropy-based measures.…”

Section: Discussionmentioning

confidence: 99%

Kolmogorov Complexity of Coronary Sinus Atrial Electrograms Before Ablation Predicts Termination of Atrial Fibrillation After Pulmonary Vein Isolation

Stępień

Kuklik

Żebrowski

et al. 2019

Entropy

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Atrial fibrillation (AF) is related to a very complex local electrical activity reflected in the rich morphology of intracardiac electrograms. The link between electrogram complexity and efficacy of the catheter ablation is unclear. We test the hypothesis that the Kolmogorov complexity of a single atrial bipolar electrogram recorded during AF within the coronary sinus (CS) at the beginning of the catheter ablation may predict AF termination directly after pulmonary vein isolation (PVI). The study population consisted of 26 patients for whom 30 s baseline electrograms were recorded. In all cases PVI was performed. If AF persisted after PVI, ablation was extended beyond PVs. Kolmogorov complexity estimated by Lempel–Ziv complexity and the block decomposition method was calculated and compared with other measures: Shannon entropy, AF cycle length, dominant frequency, regularity, organization index, electrogram fractionation, sample entropy and wave morphology similarity index. A 5 s window length was chosen as optimal in calculations. There was a significant difference in Kolmogorov complexity between patients with AF termination directly after PVI compared to patients undergoing additional ablation (p < 0.01). No such difference was seen for remaining complexity parameters. Kolmogorov complexity of CS electrograms measured at baseline before PVI can predict self-termination of AF directly after PVI.

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Characterization of rate and regularity of ventricular response during atrial tachyarrhythmias. Insight on atrial and nodal determinants

Abstract: The identification of the determinants of ventricular rate and regularity may have significant implications in the development of efficacious rate control strategies in patients with atrial tachyarrhythmias.

Cited by 13 publications

References 53 publications

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

Kolmogorov Complexity of Coronary Sinus Atrial Electrograms Before Ablation Predicts Termination of Atrial Fibrillation After Pulmonary Vein Isolation

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