@inproceedings{e5b5bff1f2714a86b7bdde12ce2294cc,
title = "Comparison of Activation Times Estimation for Potential-Based ECG Imaging",
abstract = "Activation times (AT) represent the sequence of cardiac depolarization and are one of the most important parameters of cardiac electrical activity. However, estimation of ATs is challenging due to multiple sources of noise. If ATs are estimated from signals reconstructed using electrocardiographic imaging (ECGI), additional problems can arise due to over-smoothing or ambiguities in the inverse problem. Resulting AT maps can show falsely homogeneous regions or artificial lines of block. As ATs are not only important clinically, but are also used for evaluation of ECGI, it is important to understand where these errors come from.We present results from a community effort to compare AT estimation methods on a common dataset of simulated ventricular pacings. ECGI reconstructions were performed in terms of transmembrane voltages as well as epiendo and pericardial potentials, all using 2nd-order Tikhonov and 6 regularization parameters. ATs were then estimated by the participants and compared to the truth.While the pacing site had the largest effect on AT correlation coefficients (CC), there were also differences between methods and source models that were poorly reflected in CCs. Results indicate that artificial lines of block are most severe for purely temporal methods. Compared to the other source models, ATs estimated from transmembrane voltages are more precise and less prone to artifacts.",
author = "Matthias Schaufelberger and Steffen Schuler and Bear, {Laura R.} and Matthijs Cluitmans and Jaume Coll-Font and Onak, {{\"O}nder Nazim} and Olaf D{\"o}ssel and Dana Brooks",
note = "Publisher Copyright: {\textcopyright} 2019 Creative Commons.",
year = "2019",
month = sep,
doi = "10.22489/cinc.2019.379",
language = "English",
volume = "46",
series = "Computing in Cardiology",
publisher = "IEEE Computer Society",
pages = "1--4",
booktitle = "Computing in Cardiology (CinC)",
}