Key result
Data fusion framework outperforms prior methods with ~90% accuracy locating rotor tips in simulated atrial electrograms.
Why the study?
Rotor localization from fractionated EGMs is crucial for AF ablation, but physiological noise degrades EGM processing tools and affects ablation outcomes.
Does a data fusion framework based on information quality assessment improve the accuracy of rotor tip location in simulated atrial fibrillation electrograms compared to existing methods?
Does a data fusion framework based on information quality assessment improve the accuracy of rotor tip location in simulated atrial fibrillation electrograms compared to existing methods?
A novel data fusion framework incorporating information quality assessment improves the accuracy of rotor tip localization in simulated AF electrograms, potentially aiding future ablation targeting.
Answers are grounded in this paper and cite their sources.
May support rotor mapping advances in AF simulations; leaves open in vivo and clinical validation.
Persistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application.
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Becerra et al. (2025) studied Persistent atrial fibrillation. Data fusion framework with information quality assessment vs. Existing EGM-based rotor detection methods was evaluated on Accuracy of rotor tip location estimation. A data fusion framework based on information quality assessment achieved approximately 90% accuracy in locating rotor tips from simulated atrial electrograms, outperforming existing methods.
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