Authors: Junseok Lee, Yeonho Yoo, Jinkyu Kim, Dosun Lim, Gyeongsik Yang, Chuck Yoo
Venue: The 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), Acceptance rate 29%, NRF BK IF: 1, Daejeon, Korea, 2025. (2025)
Abstract: With the rise of wearable IoT devices such as smartwatches and smart rings, ECG signals have become more accessible and made cardiovascular monitoring a reality. However, analyzing the ECG signals for complex conditions, such as bundle branch blocks and myocardial infarction, requires multi-lead ECG data. Although various deep learning models for ECG reconstruction have been proposed, they are computationally expensive and unsuitable on resource-constrained wearable IoT devices. To address this challenge, we propose mEcgNet, a parameter-efficient model for reconstructing 12-lead ECG signals from a single lead. mEcgNet introduces a modular deep learning architecture for parameter efficiency and separates the single lead-I signal into multiple frequency segments to improve accuracy. Our experiments demonstrate that mEcgNet significantly reduces the number of parameters and inference time by ∼23.1× and ∼5.4×, respectively, compared to existing state-of-the-art models. Furthermore, it reduces the reconstruction error by ∼22.1%, demonstrating its high accuracy and efficiency.