Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Principal component analysis of atrial fibrillation: Inclusion of posterior ECG leads does not improve correlation with left atrial activity

Background Lead V? is routinely analysed due to its large amplitude AF waveform. V? correlates strongly with right atrial activity but only moderately with left atrial activity. Posterior lead V? correlates strongest with left atrial activity. Aims (1) To establish whether surface dominant AF frequency (DAF) calculated using principal component analysis (PCA) of a modified 12-lead ECG (including posterior leads) has a stronger correlation with left atrial activity compared to the standard ECG. (2) To assess the contribution of individual ECG leads to the AF principal component in both ECG configurations. Methods Patients were assigned to modified or standard ECG groups. In the modified ECG, posterior leads V? and V? replaced V? and V?. AF waveform was extracted from one-minute surface ECG recordings using PCA. Surface DAF was correlated with intracardiac DAF from the high right atrium (HRA), coronary sinus (CS) and pulmonary veins (PVs). Results 96 patients were studied. Surface DAF from the modified ECG did not have a stronger correlation with left atrial activity compared to the standard ECG. Both ECG configurations correlated strongly with HRA, CS and right PVs but only moderately with left PVs. V? contributed most to the AF principal component in both ECG configurations

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation

This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present. In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF. Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings. Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods. In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable. In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%. The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters. In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment

Comparison of atrial rhythm extraction techniques for the estimation of the main atrial frequency from the 12-lead electrocardiogram in atrial fibrillation

) Hz (Valencia) and 6.5 (5.9 -8.2) Hz (Newcastle). There were no significant differences between the atrial frequencies estimated by each of the techniques

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. 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L., Lecannelier, E. A., Pino, E. J., & Rojas, A. J. (2013). Atrial activity selection for atrial fibrillation ECG recordings. Computers in Biology and Medicine, 43(10), 1628-1636. doi:10.1016/j.compbiomed.2013.08.002Fauchier, L., Villejoubert, O., Clementy, N., Bernard, A., Pierre, B., Angoulvant, D., … Lip, G. Y. H. (2016). Causes of Death and Influencing Factors in Patients with Atrial Fibrillation. The American Journal of Medicine, 129(12), 1278-1287. doi:10.1016/j.amjmed.2016.06.045Fujiki, A., Sakabe, M., Nishida, K., Mizumaki, K., & Inoue, H. (2003). Role of Fibrillation Cycle Length in Spontaneous and Drug-Induced Termination of Human Atrial Fibrillation. Circulation Journal, 67(5), 391-395. doi:10.1253/circj.67.391Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Roonizi, E. K., & Sassi, R. (2017). An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation. IEEE Journal of Biomedical and Health Informatics, 21(6), 1573-1580. doi:10.1109/jbhi.2016.2625338Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Langley, P. (2015). Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation. Entropy, 17(12), 6397-6411. doi:10.3390/e17096397Langley, P., Rieta, J. J., Stridh, M., Millet, J., Sornmo, L., & Murray, A. (2006). Comparison of Atrial Signal Extraction Algorithms in 12-Lead ECGs With Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 53(2), 343-346. doi:10.1109/tbme.2005.862567Lee, J., Song, M., Shin, D., & Lee, K. (2012). 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Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Computers in Biology and Medicine, 43(2), 154-163. doi:10.1016/j.compbiomed.2012.11.007McSharry, P. E., Clifford, G. D., Tarassenko, L., & Smith, L. A. (2003). A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 50(3), 289-294. doi:10.1109/tbme.2003.808805Nault, I., Lellouche, N., Matsuo, S., Knecht, S., Wright, M., Lim, K.-T., … Haïssaguerre, M. (2009). Clinical value of fibrillatory wave amplitude on surface ECG in patients with persistent atrial fibrillation. Journal of Interventional Cardiac Electrophysiology, 26(1), 11-19. doi:10.1007/s10840-009-9398-3Petrenas, A., Marozas, V., Sološenko, A., Kubilius, R., Skibarkiene, J., Oster, J., & Sörnmo, L. (2017). Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes. 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Extraction and analysis of T waves in electrocardiograms during atrial flutter

Analysis of T waves in the electrocardiogram (ECG) is an essential clinical tool for diagnosis, monitoring and followup of patients with heart dysfunction. During atrial flutter, this analysis has been so far limited by the perturbation of flutter waves superimposed over the T wave. This paper presents a method based on missing data interpolation for eliminating flutter waves from the ECG during atrial flutter. To cope with the correlation between atrial and ventricular electrical activations, the CLEAN deconvolution algorithm was applied to reconstruct the spectrum of the atrial component of the ECG from signal segments corresponding to TQ intervals. The location of these TQ intervals, where the atrial contribution is presumably dominant, were identified iteratively. The algorithm yields the extracted atrial and ventricular contributions to the ECG. Standard T-wave morphology parameters (T-wave amplitude, T peak – T end duration, QT interval) were measured. This technique was validated using synthetic signals, compared to average beat subtraction in a patient with a pacemaker and tested on pseudo-orthogonal ECGs from patients in atrial flutter. Results demonstrated improvements in accuracy and robustness of T-wave analysis as compared to current clinical practice

Effects of Two Different Catheter Ablation Techniques on Spectral Characteristics of Atrial Fibrillation

ObjectivesThe aim of this study was to determine the effects of circumferential pulmonary vein ablation (CPVA) and electrogram-guided ablation (EGA) on the spectral characteristics of atrial fibrillation (AF) and the relationship between changes in dominant frequency (DF) and clinical outcome.BackgroundCircumferential pulmonary vein ablation and EGA have been used to eliminate AF. Spectral analysis may identify high-frequency sources.MethodsIn 84 consecutive patients, CPVA (n = 42) or EGA (n = 42) was performed for paroxysmal (n = 49) or persistent (n = 35) AF. During EGA, complex electrograms were targeted. Lead V1and electrograms from the left atrium and coronary sinus were analyzed to determine the DF of AF before and after ablation.ResultsThe left atrial DF was higher in persistent (5.83 ± 0.86 Hz) than paroxysmal AF (5.33 ± 0.76 Hz, p = 0.03). There was a frequency gradient from the left atrium to the coronary sinus (p = 0.02). Circumferential pulmonary vein ablation and EGA resulted in a similar decrease in DF (18 ± 17% vs. 17 ± 15%, p = 0.8). During a mean follow-up of 9 ± 6 months, the change in DF after CPVA was similar among patients with and without recurrent AF. An acute decrease in DF after EGA was associated with freedom from recurrent AF only in patients with persistent AF (19 ± 14% vs. 3 ± 6%, p = 0.02).ConclusionsBoth CPVA and EGA decrease the DF of AF, consistent with elimination of high-frequency sources. Whereas the efficacy of EGA is associated with a decrease in DF in patients with persistent AF, the efficacy of CPVA is independent of changes in DF. This suggests that CPVA and EGA eliminate different mechanisms in the genesis of persistent AF