Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power

Corino, Valentina D. A.; Rivolta, Massimo W.; Sassi, Roberto; Lombardi, Federico; Mainardi, Luca

doi:10.1016/j.medengphy.2013.07.010

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…In spite of the fact that a promising application of CGCD to AF EGMs was revealed, this work contained only unipolar EGMs, which are quite unusual in daily electrophysiological procedures of AF ablation. In addition, the risk of significant ventricular contamination under unipolar recordings is high [ 65 ], and despite the QRS subtraction that was performed, four seconds is not an adequate interval for an effective ventricular removal [ 40 , 66 ]. As ventricular deflections can be even larger than atrial in unipolar EGMs, the corresponding CGCD values may very likely appear altered.…”

Section: Discussionmentioning

confidence: 99%

Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Vraka

Hornero

Bertomeu-González³

et al. 2020

Entropy

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Atrial fibrillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its first line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identification requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classification accuracy of 100% in Group 1 and 84.0–85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identified as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modification.

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Section: Discussionmentioning

confidence: 99%

Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Vraka

Hornero

Bertomeu-González³

et al. 2020

Entropy

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“…Also, these methods are more suitable to generate body surface ECGs rather than EGMs. The work in [41] generates atrial EGMs by simulating the activation of the atrial fibers from the movement of a single dipole, which is less realistic. In this work we focus on the atrial cell level to model the action potential during atrial fibrillation and extend it to the two-dimensional monodomain tissue.…”

Section: Discussionmentioning

confidence: 99%

Graph-Time Spectral Analysis for Atrial Fibrillation

Ming¹,

Isufi²,

Groot³

et al. 2019

Preprint

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Atrial fibrillation is a clinical arrhythmia with multifactorial mechanisms still unresolved. Timefrequency analysis of epicardial electrograms has been investigated to study atrial fibrillation. However, deeper understanding of atrial fibrillation can be achieved if the spatial dimension can be incorporated. Unfortunately, the physical models describing the spatial relations of atrial fibrillation signals are complex and non-linear; hence, the conventional signal processing techniques to study electrograms in the joint space, time, and frequency domain are less suitable. In this study, we wish to put forward a radically different approach to analyze atrial fibrillation with a higher-level model. This approach relies on graph signal processing to represent the spatial relations between epicardial electrograms and put forward a graph-time spectral analysis for atrial fibrillation. To capture the frequency content along both the time and graph domain, we proposed the joint graph and short-time Fourier transform. The latter allows us to analyze the spatial variability of the electrogram temporal frequencies. With this technique, we have found that the spatial variation of the atrial electrograms decreases during atrial fibrillation due to the reduction of the high temporal frequencies of the atrial waves. The proposed analysis further confirms that the ventricular activity is smoother over the atrial area compared with the atrial activity. Besides using the proposed graph-time analysis to conduct a first study on atrial fibrillation, we applied it to the cancellation of ventricular activity from atrial electrograms. Experimental results on simulated and real data further corroborate the findings in this atrial fibrillation study.

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“…The total stimulation signal length was set to 8 ms, with the first part of the biphasic signal being 2 ms in accordance with the clinical stimulation setup. Atrial activities were modeled following Corino et al (2013) based on a moving dipole in an infinite homogeneous conductor. The atrial activity synthetic signal morphology was scaled to have an amplitude of 2 mV and duration of 35 ms ( Figure 4A ).…”

Section: Methodsmentioning

confidence: 99%

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution

et al. 2021

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BackgroundRate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity.Methods and ResultsThe S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude.ConclusionThe proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.

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Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power

Cited by 15 publications

References 20 publications

Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Graph-Time Spectral Analysis for Atrial Fibrillation

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution

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