Characterization of atrial arrhythmias in body surface potential mapping: A computational study

Marques, Victor G; Rodrigo, Miguel; Guillem, María S.; Salinet, João

doi:10.1016/j.compbiomed.2020.103904

Cited by 16 publications

(17 citation statements)

References 28 publications

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“…In 2D models of human atrial tissue and recorded in isolated sheep hearts, phase maps have been used to detect relatively stable re-entries and foci (Zlochiver et al, 2008). Additionally, as mentioned in the introduction, computational studies suggest that BSPM (without ECGI) may already provide a characterization of AF mechanisms, by allowing the noninvasive characterization of rotors and their location in the atria, and showing large areas of highest dominant frequency, with values close to the frequency of rotation of the atrium rotor (Marques et al, 2020a). Conversely, the mechanism of multiple and continuous intra-atrial re-entry is generally observed with unstable and short-lived patterns when compared to the ectopic and rotor mechanisms.…”

Section: Validation Of Ecgi During Af In the Time And Phase Domainsmentioning

confidence: 99%

“…This suggests that body surface potentials, for example captured by the clinical electrocardiogram (ECG), may retain information about the underlying electro-structural substrate of AF, and could be exploited for its identification and characterization (Guillem et al, 2013). In this respect, Marques et al showed that analyses in the frequency and phase domain of Body Surface Potential Mapping (BSPM) recordings allowed the non-invasive characterization of rotors and their localization in the atria, and helped distinguish rotors from other mechanism (like ectopic foci or macro re-entrant circuits; Marques et al, 2020a). However, the electrical activity recorded on the body-surface is a smoothed and attenuated combination of all electrical activity at the level of the heart surface, which may limit the possibility to accurately identify and characterize AF mechanisms by only using the ECG (van Oosterom, 2012).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Validation Of Ecgi During Af In the Time And Phase Domainsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…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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“…The number of electrodes can vary from 32 to 256 and most study organization strive to position more electrodes on the front of the torso as there are considerably larger potential changes on the front (Rodrigo et al, 2017). By using BSPM, several studies have shown an improved diagnosis (Lefebvre and Hoekstra, 2007) and to characterize different cardiac arrhythmias (Marques et al, 2020). Other applications include the automatic assessment of Electrogram quality (Costoya-Sánchez et al, 2020).…”

Section: Cardiac Rhythm Characterizationmentioning

confidence: 99%

“…Briefly, DF was defined as the frequency that presented the highest power calculated with a Welch's periodogram to determine the local DFs with a spectral resolution of 0.01Hz (Guillem et al, 2013). Rotor location was carried out by identification of SP (SP) in the phase map obtained with the Hilbert Transform as described in previous publications of the group (Marques et al, 2020). Phase values were obtained along 3 different circles surrounding each evaluated point, and six to twelve points per circle were used for the phase analysis in which the signal was interpolated by a weighted average of the neighboring nodes, being d2 the weight for each node and d the distance between the nodes.…”

Section: Data Processing and Cleaningmentioning

confidence: 99%

Definition of a Sensitivity Profile for Drug Treatment and Identification on Clinical Biomarkers in Atrial Fibrillation

Nava¹

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Atrial Fibrillation (AF) is one of the most common arrhythmias in clinical practice and thus far, the electrophysiological mechanisms underlying its initiation and maintenance are not fully understood. The study of such mechanism including clinical information, computational models and artificial intelligence (AI) algorithms that enable the identification of new patterns for the personalization of the treatments is key to unveil the characteristics of the arrhythmia.

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Characterization of atrial arrhythmias in body surface potential mapping: A computational study

Cited by 16 publications

References 28 publications

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

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

Definition of a Sensitivity Profile for Drug Treatment and Identification on Clinical Biomarkers in Atrial Fibrillation

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