116 citations · 120 across the 5 of their papers we have counts for
5 papers
Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring
Chang Min Hyun, Tae-Geun Kim, Kyounghun Lee
This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS)…
Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography
Chang Min Hyun, Tae Jun Jang, Jeongchan Nam +3
Owing to recent advances in thoracic electrical impedance tomography, a patient's hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a…
A learning-based method for solving ill-posed nonlinear inverse problems: a simulation study of Lung EIT
Jin Keun Seo, Kang Cheol Kim, Ariungerel Jargal +2
This paper proposes a new approach for solving ill-posed nonlinear inverse problems. For ease of explanation of the proposed approach, we use the example of lung electrical impedan…
A Fidelity-embedded Regularization Method for Robust Electrical Impedance Tomography
Kyounghun Lee, Eung Je Woo, Jin Keun Seo
Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applicat…
Electrical impedance tomography-based pressure-sensing using conductive membrane
Habib Ammari, Kyungkeun Kang, Kyounghun Lee +1
This paper presents a mathematical framework for a flexible pressure-sensor model using electrical impedance tomography (EIT). When pressure is applied to a conductive membrane pat…