4 papers
RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning
Cheol-Hui Lee, Hwa-Yeon Lee, Dong-Joo Kim
The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabe…
PhysioME: A Robust Multimodal Self-Supervised Framework for Physiological Signals with Missing Modalities
Cheol-Hui Lee, Hwa-Yeon Lee, Min-Kyung Jung +1
Missing or corrupted modalities are common in physiological signal-based medical applications owing to hardware constraints or motion artifacts. However, most existing methods assu…
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Cheol-Hui Lee, Hakseung Kim, Byung C. Yoon +1
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disord…
Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection
Tae-Seong Han, Jae-Wook Heo, Hakseung Kim +5
Electrocardiography (ECG) signals are frequently degraded by noise, limiting their clinical reliability in both conventional and wearable settings. Existing methods for addressing…