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20212023
most citedLead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram

4 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.QM2023★ 3 cited

ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

Jungwoo Oh, Gyubok Lee, Seongsu Bae +2

Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datas…

cs.CL2023

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

Hyunseung Chung, Jiho Kim, Joon-myoung Kwon +3

Electrocardiogram (ECG) synthesis is the area of research focused on generating realistic synthetic ECG signals for medical use without concerns over annotation costs or clinical d…

eess.SP2022

Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels

Radhika Dua, Jiyoung Lee, Joon-myoung Kwon +1

Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise ar…

cs.LG2022★ 4 cited

Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram

Jungwoo Oh, Hyunseung Chung, Joon-myoung Kwon +2

In recent years, self-supervised learning methods have shown significant improvement for pre-training with unlabeled data and have proven helpful for electrocardiogram signals. How…

eess.SP2021★ 4 cited

Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads

Yong-Yeon Jo, Young Sang Choi, Jong-Hwan Jang +1

The electrocardiogram (ECG) records electrical signals in a non-invasive way to observe the condition of the heart, typically looking at the heart from 12 different directions. Sev…