4 citations · 5 across the 9 of their papers we have counts for
9 papers
RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals
Yong-Yeon Jo, Junho Song, Joon-myoung Kwon
Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online per…
ExECG: An Explainable AI Framework for ECG models
Jong-Hwan Jang, Yong-yeon Jo
Deep learning has enabled ECG diagnostic models with strong performance in tasks such as arrhythmia classification and abnormality detection. However, accuracy alone is insufficien…
CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
Jong-Hwan Jang, Junho Song, Yong-Yeon Jo
Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models (AI-ECG) into clinical practice, we introduce a fram…
ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
Jin Yu, JaeHo Park, TaeJun Park +6
Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and conveni…
New Test-Time Scenario for Biosignal: Concept and Its Approach
Yong-Yeon Jo, Byeong Tak Lee, Beom Joon Kim +3
Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks lik…
TADA: Temporal Adversarial Data Augmentation for Time Series Data
Byeong Tak Lee, Joon-myoung Kwon, Yong-Yeon Jo
Domain generalization aim to train models to effectively perform on samples that are unseen and outside of the distribution. Adversarial data augmentation (ADA) is a widely used te…
CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains
Junho Song, Jong-Hwan Jang, DongGyun Hong +2
Electrocardiogram (ECG) diagnosis remains challenging due to limited labeled data and the need to capture subtle yet clinically meaningful variations in rhythm and morphology. We p…
Optimizing Neural Network Scale for ECG Classification
Byeong Tak Lee, Yong-Yeon Jo, Joon-Myoung Kwon
We study scaling convolutional neural networks (CNNs), specifically targeting Residual neural networks (ResNet), for analyzing electrocardiograms (ECGs). Although ECG signals are t…
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…