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定向图映射在检测纤维化组织中添加噪声的模拟 2D 蜿蜒转子方面优于相位映射。

Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise.

机构信息

Department of Physics and Astronomy, Ghent University, Ghent, Belgium.

Department of Physics and Astronomy, Ghent University, Ghent, Belgium.

出版信息

Comput Biol Med. 2024 Mar;171:108138. doi: 10.1016/j.compbiomed.2024.108138. Epub 2024 Feb 16.

DOI:10.1016/j.compbiomed.2024.108138
PMID:38401451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10966475/
Abstract

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F-scores remain high, yielding F≥0.931 as opposed to the best PM F≥0.635 across all 64 simulations.

摘要

心律失常,如心房颤动 (AF),已被认为与折返或转子有关。转子是心肌组织中的兴奋波,它围绕其不应期尾部缠绕,导致比正常更快的周期性兴奋。转子中心的检测对于指导消融策略治疗心律失常至关重要。检测转子中心最流行的技术是相位映射 (PM),它检测来自信号相位的相位奇点。这种方法已被证明容易出错,尤其是在纤维化组织和时间噪声的情况下。最近,一种称为有向图映射 (DGM) 的新技术被开发出来,通过创建兴奋网络来检测旋转活动,如转子。本研究旨在比较先进的 PM 技术与 DGM 对 64 个模拟 2D 蜿蜒转子在不同纤维化组织和时间噪声水平下的转子检测性能。采用了四种策略来比较 PM 和 DGM 的性能。这些策略包括视觉分析、F 分数和距离分布的比较,以及使用中值 McNemar 检验计算 p 值。结果表明,在低蜿蜒、纤维化和噪声的情况下,PM 和 DGM 都能取得优异的结果,并且可以相互比较。然而,在高蜿蜒、纤维化和噪声的情况下,PM 不可避免地容易出错,主要表现为过多的假阳性,导致精度低。相比之下,DGM 对这些因素更具鲁棒性,因为 F 分数保持较高,在所有 64 个模拟中,F≥0.931,而最佳 PM F≥0.635。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/914a6ad5bcf5/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/28a39bd09d78/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/e8a4250908dd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/a9b3e1df87ba/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/0a4697cf8b0a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/60dce743b023/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/6426e1da134f/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/38ba6f9708f5/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/621554bba2e8/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/f5562f68c00e/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/914a6ad5bcf5/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/28a39bd09d78/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/e8a4250908dd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/a9b3e1df87ba/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/0a4697cf8b0a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/60dce743b023/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/6426e1da134f/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/38ba6f9708f5/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/621554bba2e8/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/f5562f68c00e/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee1a/10966475/914a6ad5bcf5/gr10.jpg

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Topological charge-density method of identifying phase singularities in cardiac fibrillation.拓扑电荷密度法在心脏纤颤中的相位奇点识别。
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Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid and Dataset.
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Front Physiol. 2021 Jul 5;12:699291. doi: 10.3389/fphys.2021.699291. eCollection 2021.
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The openCARP simulation environment for cardiac electrophysiology.openCARP 心脏电生理模拟环境。
Comput Methods Programs Biomed. 2021 Sep;208:106223. doi: 10.1016/j.cmpb.2021.106223. Epub 2021 Jun 8.
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