4 papers
AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset
Yongbin Lee, Ki H. Chon
Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased risks of stroke and heart failure. The growing availability of wearable and portable…
Memory-Efficient EDA Denoising via Knowledge Distillation for Wearable IoT Under Severe Motion Artifacts and Underwater Conditions
Yongbin Lee, Andrew Peitzsch, Youngsun Kong +4
Electrodermal activity (EDA) is widely used in wearable Internet of Medical Things (IoMT) systems for continuous health monitoring, including autonomic assessment. However, EDA sig…
ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition
Yongbin Lee, Youngsun Kong, Ki H. Chon
Electrodermal activity (EDA) is a widely used physiological signal for assessing sympathetic nervous activity, such as arousal, stress, and pain. However, reliable decomposition in…
Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture
Yongbin Lee, Ki H. Chon
Atrial fibrillation (AF) is the most common arrhythmia, increasing the risk of stroke, heart failure, and other cardiovascular complications. While AF detection algorithms perform…