Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effecthttps://www.embs.org/tbme/wp-content/uploads/sites/19/2023/10/TBME-01182-2022-Website-Image-Resized.gif740416IEEE Transactions on Biomedical Engineering (TBME)IEEE Transactions on Biomedical Engineering (TBME)//www.embs.org/tbme/wp-content/uploads/sites/19/2022/06/ieee-tbme-logo2x.png
This study explains how to calibrate the parameters governing the mechanical boundary conditions of a thoracic aorta model with ascending aortic aneurysm integrating 3D and 2D magnetic resonance images.
Author(s)3: Wilson W. Good, Karli K. Gillette, Brian Zenger, Jake A. Bergquist, Lindsay C. Rupp, Jess Tate, Devan Anderson, Matthias A.F. Gsell, Gernot Plank, Rob S. MacLeod
Estimation and validation of cardiac conduction velocity and wavefront reconstruction using epicardial and volumetric datahttps://www.embs.org/tbme/wp-content/uploads/sites/19/2021/10/TBME-02066-2020-Highlight-Image.gif170142IEEE Transactions on Biomedical Engineering (TBME)IEEE Transactions on Biomedical Engineering (TBME)//www.embs.org/tbme/wp-content/uploads/sites/19/2022/06/ieee-tbme-logo2x.png
Cardiac conduction velocity (CV) is an important electrophysiological property that describes the speed and direction of electrical propagation through the heart. Accurate CV measurements provide a valuable quantitative description of electrical propagation that can help identify diseased tissue substrate and stratify patient risk. In this study we explored a range of techniques for estimating epicardial and volumetric CV and validated the performance of the techniques using whole heart image-based computational modeling. The CV estimation techniques implemented in this study (streamlines, triangulation, inverse-gradient) produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.
Author(s)3: Joseph L. Betthauser, John T. Krall, Shain G. Bannowsky, Gyorgy Levay, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networkshttps://www.embs.org/tbme/wp-content/uploads/sites/19/2020/05/TBME-00364-2019-Highlight-Image.gif170177IEEE Transactions on Biomedical Engineering (TBME)IEEE Transactions on Biomedical Engineering (TBME)//www.embs.org/tbme/wp-content/uploads/sites/19/2022/06/ieee-tbme-logo2x.png
Movement prediction from EMG can be performed by compressing a short window of EMG into a feature-encoding that is meaningful for classification— an approach that can cause erratic prediction behavior. Temporal convolutional networks (TCN) leverage temporal information from EMG to achieve superior predictions for 3 simultaneous degrees-of-freedom that are more accurate and stable, have a very low response delay, and allow for novel types of interactive training. Addressing EMG decoding as a sequential prediction problem requires a new set of considerations that will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems.
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