Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem.

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-09-24 DOI:10.1088/1361-6579/ad74d6
Y Serinagaoglu Dogrusoz, L R Bear, J A Bergquist, A S Rababah, W Good, J Stoks, J Svehlikova, E van Dam, D H Brooks, R S MacLeod
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Abstract

Objective.This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.Approach.We utilized experimental data from two distinct centers. Langendorff-perfused pig (n= 2) and dog (n= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.Main results.The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.Significance.This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.

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评估在解决反向心电图问题时对不良导联进行插值的五种方法。
目的:本研究旨在评估基于心外膜电位的心电图成像(ECGI)对去除或插入不良导联的敏感性。我们利用了两个不同中心的实验数据。朗根多夫灌注的猪(n=2)和狗(n=2)心脏被悬挂在人体躯干形状的水箱中,并从心室开始起搏。根据临床经验设计了六种不同的不良导联配置。采用五种内插法估算缺失数据。零阶提霍诺夫正则化用于解决完整数据、去除坏导联的数据和插值数据的逆问题。我们使用多个指标评估了插值心电信号和心电图成像重建的质量,比较了插值方法的性能以及去除坏导联和插值对心电图成像的影响。心电图插值的性能与心电图成像重建密切相关。在各种插值技术中,混合法的性能最好,紧随其后的是反向前插法和克里金法。位于躯干高振幅/高梯度区域的坏导联严重影响了心电图成像重建,即使插值误差很小。选择去除还是插值坏导联取决于缺失导联的位置和对插值性能的信心。如果存在不确定性,移除坏导联是更安全的选择,尤其是当坏导联位于高振幅/高梯度区域时。在必须进行插值的情况下,推荐使用不需要训练的反向前法和克里金法。本研究首次全面评估了心电图成像中插值与去除坏导联的优缺点,为心电图成像性能提供了宝贵的见解。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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