Sein Jeung

ORCID: 0000-0002-0247-087X
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About
Contact & Profiles
Research Areas
  • Functional Brain Connectivity Studies
  • Cardiac tumors and thrombi
  • Atrial Fibrillation Management and Outcomes
  • Cardiac electrophysiology and arrhythmias
  • Time Series Analysis and Forecasting
  • EEG and Brain-Computer Interfaces
  • Neural dynamics and brain function
  • Central Venous Catheters and Hemodialysis
  • Transplantation: Methods and Outcomes
  • ECG Monitoring and Analysis
  • Spatial Cognition and Navigation
  • Constraint Satisfaction and Optimization
  • Context-Aware Activity Recognition Systems

Technische Universität Berlin
2022-2024

Max Planck Institute for Human Cognitive and Brain Sciences
2022

Norwegian University of Science and Technology
2022

Sejong General Hospital
2020

Asan Medical Center
2018

University of Ulsan
2018

Ulsan College
2018

Abstract Advancements in hardware technology and analysis methods allow more mobility electroencephalography (EEG) experiments. Mobile Brain/Body Imaging (MoBI) studies may record various types of data such as motion or eye tracking addition to neural activity. Although there are options available analyze EEG a standardized way, they do not fully cover complex multimodal from mobile We thus propose the BeMoBIL Pipeline, an easy-to-use pipeline MATLAB that supports time-synchronized handling...

10.1101/2022.09.29.510051 preprint EN cc-by bioRxiv (Cold Spring Harbor Laboratory) 2022-09-30

We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological The goal of Motion-BIDS make interoperable across different laboratories with other modalities in behavioral research. To this end, standardizes format metadata structure. It describes how document experimental details, considering diversity hardware software systems This promotes findable, accessible, interoperable,...

10.1038/s41597-024-03559-8 article EN cc-by Scientific Data 2024-07-02

Abstract Background Anemia changed the morphology of electrocardiography (ECG), and researchers suggested that mismatching oxygen demand supply in myocardium affects ECG Purpose A deep-learning-based algorithm (DLA) enables non-invasive anemia screening from electrocardiograms (ECGs) may improve detection anemia. Methods DLA was developed using 57,435 ECGs 31,898 patients internally validated 7,369 taken at one hospital. External validation performed 4,068 admitted another Three types were...

10.1093/ehjci/ehaa946.3446 article EN European Heart Journal 2020-11-01
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