Electrocardiographic imaging including intracardiac information to achieve accurate global mapping during atrial fibrillation

Cámara-Vázquez, Miguel Ángel; Hernández-Romero, Ismael; Rodrigo, Miguel; Alonso‐Atienza, Felipe; Figuera, Carlos; Morgado, Eduardo; Atienza, Felipe; Guillem, María S.; Climent, Andreu Μ.; Barquero-Pérez, Óscar

doi:10.1016/j.bspc.2020.102354

Cited by 4 publications

(5 citation statements)

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“…Bayesian regularization, which makes use of a priori information [ 81 ], has been shown to outperform other methods for ECGI resolution, although a priori information is not a real-case scenario in most of the applications [ 61 ]. Again in the context of AF, we have shown that the incorporation of the information from intracardiac electrograms (EGMs) may also outperform other ECGI resolution methods [ 82 ]. However, if a priori data is not available, zero-order Tikhonov with L-curve optimization was found to be the best approach, even with constant regularization parameters for AF signals.…”

Section: Regularization and Regularization Parameter Optimization In ...mentioning

confidence: 99%

“…In this direction, we have recently proposed a new regularization technique that combines noninvasive and invasive recordings, illustrated in Fig. 5 [ 82 ]. This approach may overcome some of the limitations of both invasive and noninvasive atrial characterization and lead to a more accurate identification of AF drivers.…”

Section: What Is Next?mentioning

confidence: 99%

“…

Fig. 5 Comparison of reconstructions (epicardial potentials, dominant frequencies, phases, and phase singularities) between L0 Tikhonov and a new constrained Tikhonov regularization technique that combines noninvasive and invasive recordings, adapted from [ 82 ] …”

Section: What Is Next?mentioning

confidence: 99%

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Electrocardiographic imaging in the atria

Hernández-Romero

Molero

Fambuena-Santos

et al. 2022

Med Biol Eng Comput

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The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging. Complications can arise when trying to describe the atrial mechanisms that lead to abnormal propagation patterns, premature or tachycardic beats, and reentrant arrhythmias. This review addresses the various ECGI methodologies, regularization methods, and post-processing techniques used in the atria, as well as the context in which they are used. The current advantages and limitations of ECGI in the fields of research and clinical diagnosis of atrial arrhythmias are outlined. In addition, areas where ECGI efforts should be concentrated to address the associated unsatisfied needs from the atrial perspective are discussed. Graphical abstract

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Section: Regularization and Regularization Parameter Optimization In ...mentioning

confidence: 99%

Section: What Is Next?mentioning

confidence: 99%

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Electrocardiographic imaging in the atria

Hernández-Romero

Molero

Fambuena-Santos

et al. 2022

Med Biol Eng Comput

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“…On the other hand, Vanheusden et al found significant differences in highest dominant frequency values between intra-cardiac and torso signals, when evaluating the highest dominant frequency behavior in AF patients using simultaneously measured virtual atrial electrogram signals from both atria and BSPM (Vanheusden et al, 2019). As for ECGI specifically, both mathematical models and clinical recordings have been used to validate the accuracy of non-invasive mapping to identify dominant frequencies (Figure 4; Figuera et al, 2016;Pedrón-Torrecilla et al, 2016;Zhou et al, 2016;Rodrigo et al, 2017a;Cámara-Vázquez et al, 2021). Since computation of highest dominant frequency requires a time segment, it is obtained as the average over a certain period, and appears to be a more robust parameter in AF patients than instantaneous signal properties such as phase (Pedrón-Torrecilla et al, 2016).…”

Section: Validation Of Ecgi During Af In the Spectral Domainmentioning

confidence: 99%

Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value

et al. 2021

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Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

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“…ECGI has been used to identify regions of dominant frequency and assist physicians in targeting these regions for treatment. Recent studies in this domain have explored the use of ECGI to identify atrial fibrillation drivers using both simulated data and real-world clinical data [159,[184][185][186]. While these methods are still in development, the results show promise for ECGI in the context of AF.…”

Section: Deterministic Approach: Ecgimentioning

confidence: 99%

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Bergquist

Rupp

Zenger

et al. 2021

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Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

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Electrocardiographic imaging including intracardiac information to achieve accurate global mapping during atrial fibrillation

Cited by 4 publications

References 45 publications

Electrocardiographic imaging in the atria

Electrocardiographic imaging in the atria

Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

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