서동혁/김의종/조용연’s paper has been accepted in

Title: Accurate Generation of the Standard 12-Lead ECG from a Single Lead based on Inherent Principles of ECG
Author: Dong-hyuk Seo, Ui Jong Kim, Yong-Yeon Jo, Joon-myoung Kwon, Won-Yong Shin, and Sang-Wook Kim
Abstract
Wearable and portable devices enable convenient electrocardiography (ECG) acquisition, yet they typically measure only a single limb lead, limiting diagnostic utility compared to the standard 12-lead ECG used in hospitals. We study the problem of generating the full standard 12-lead ECG from a single measured lead by leveraging inherent ECG principles. Specifically, we emphasize two claims: (i) limb lead reconstruction benefits from a hybrid strategy that generates only one additional limb lead via learning and computes the remaining limb leads through vector operations; and (ii) precordial lead reconstruction is more reliable when conditioned on the full set of limb leads. Based on these claims, we propose \textbf{AURORA}, a 12-lead ECG reconstruction framework that combines learning models and deterministic vector operations to generate limb leads, followed by the reconstruction of precordial leads. To instantiate the learning components, we propose \textbf{IGUANA}, a lead-pattern learning model comprising a lead representation learner and a lead generator for precise lead-to-lead transformation. Experiments on two benchmark datasets demonstrate that AURORA consistently outperforms state-of-the-art methods and validate the effectiveness of incorporating vector operations in lead reconstruction. Our code is available at \url{https://anonymous.4open.science/r/ECG-553F}.