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基于静脉导管的异位心外膜激动标测:用于统计估计的训练数据集选择

Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation.

作者信息

Yilmaz Bülent, MacLeod Robert S, Punske Bonnie Billard, Taccardi Bruno, Brooks Dana H

机构信息

Biomedical Engineering Department of Başkent University, Ankara, Turkey.

出版信息

IEEE Trans Biomed Eng. 2005 Nov;52(11):1823-31. doi: 10.1109/TBME.2005.856243.

Abstract

A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.

摘要

大多数现有的导管心脏标测方法存在一个误差来源,即尽管涉及心外膜和心外膜下层的心律失常基质约占室性心动过速的15%,但它们无法获取心外膜电位。在这一亚组患者中,仅限于心内膜的标测技术会导致定位误差,并使后续消融手术失败。此外,基于导管的心外膜电生理研究仅限于冠状血管附近区域或需要经胸途径。我们开发了一种统计方法,可从非常低分辨率的多电极静脉导管测量中估计心外膜激活的高分辨率图。该技术需要一组先前记录的图作为训练集,因此数据库的构成成为准确性的重要决定因素。该研究的具体假设是,当训练数据集与测试搏动的数据集匹配时,估计准确性最佳,其中匹配是根据搏动的起始部位进行的。这一假设提出了优化训练集选择的方法,我们已经开发并评估了其中三种方法。其中一种方法,即高CC细化方法,能够将左心室起搏图的最早激活部位估计在距真实部位平均4.67毫米以内;在89%的病例(共231例)中,误差小于10毫米。在另一种方法,即MHC空间激活方法中,右心室起搏图(239张图)的估计误差为7.15毫米。原始图和估计图之间的平均相关系数也非常高(0.97),这表明训练数据集细化方法能够估计心外膜激活序列。这些测试结果支持了这一假设,此外,还表明这种方法在临床环境中提供完整的心外膜激活时间图的准确重建是可行的。

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