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Atrial fibrillation (AF) is the most common arrhythmia, and one of the main causes of ictus and strokes. Effective treatments for AF are still unknown, as its effects on the heart substrate have not been clearly quantified yet. One of the main lines of investigation aims at characterizing ventricular response by looking at its effects on heartbeat interval dynamics. Most of the standard approaches have focused on RR interval (RRI) histogram parameters albeit with several shortcomings, such as bin width dependence or lack of attention to the time-varying dynamical structure. In this study, we model heartbeat interval series as a history-dependent inverse Gaussian (HDIG) point process where the history for each RRI prediction is a linear regression of the previous RRIs. As opposed to classical nonparametric methods, the heart rate (HR) variability features derived from the proposed parametric model provide a physiologically more consistent characterization during AF, and can clearly discriminate AF from sinus rhythm (SR) subjects. Analysis of 36 patients affected by persistent AF and 18 controls shows that RRI distributions are more right-skewed and affected by higher variability during AF (skewness of 0.63±0.29 in AF and of 0.17±0.16 in SR, p=7.6 · 10−8; HR standard deviation of 18.73±10.64 bpm in AF and 4.66±4.75 bpm in SR, p=2.3 · 10−6). Our results demonstrate that we can extract valuable information associated with AF from RRI series by using a point process framework. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the heart rate variability (HRV), obtained by using the time-varying integral pulse frequency modulation (TVIPFM) which is well adapted to the exercise stress testing. [ABSTRACT FROM PUBLISHER]

Radiofrequency catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF). Nevertheless, its success is not always guaranteed, as selection of patients who could positively respond to this therapy does not rely on systematic criteria and still remains an open issue. Moreover, very little is known about the quantitative effects of this treatment over AF electrophysiology, so their quantitative evaluation is not a trivial task. In this contribution, ablation impact is quantified by a descriptor of fibrillatory wave (f-wave) amplitude, so far regarded as a predictor of short-term CA outcome. By means of principal component analysis (PCA), surface electrocardiogram (ECG) spatial diversity is exploited and contributions from all leads are combined to describe average f-wave peak-to-peak amplitude, whose value is automatically computed by an algorithm based on cubic spline interpolation. Our work demonstrates how CA influences f-wave amplitude during the procedure as quantified by ECG inter-lead spatial variability. In addition, we show how such variations depend on procedural outcome and the duration of the postoperative blanking period. [ABSTRACT FROM PUBLISHER]

Radiofrequency catheter ablation (CA) is increasingly employed to treat atrial fibrillation (AF), yet selection of patients who would actually benefit from this therapy still remains an open issue. The present work introduces some non-invasive quantitative parameters to discriminate between successful and failing CA procedures by exploiting the spatial diversity of the 12-lead surface ECG. They are based on the normalized mean square error (NMSE) between consecutive atrial activity ECG signal segments and their rank-n approximations determined by principal component analysis. As opposed to the single-lead approach of previous works, we consider NMSE values computed on more than one lead. One such multilead-based parameter is able to distinguish between successful and failing ablations before performing the procedure with AUC=0.86, p value=7·10−5. This study demonstrates that the proposed multilead parameters can effectively predict CA outcome and potentially contribute to more accurate patient selection strategies for this AF therapy. [ABSTRACT FROM PUBLISHER]