IEEE Transactions on Biomedical Engineering

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Estimation and validation of cardiac conduction velocity and wavefront reconstruction using epicardial and volumetric data
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... Read more
Theoretical Modeling of Photocatalytic Degradation Mechanism of Ethylene over TiO2
The photocatalytic degradation of ethylene over TiO2 has been widely studied, however, there are discrepancies between the degradation mechanisms proposed in experimental works. Some of them propose a degradation and mineralization mechanism trough ethoxide, acetaldehyde, acetic acid and finally carbon... Read more
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A Patient-Specific Computational Framework for the Argus II Implant
Goal: Retinal prosthesis performance is limited by the variability of elicited phosphenes. The stimulating electrode’s position with respect to retinal ganglion cells (RGCs) affects both perceptual threshold and phosphene shape. We created a modeling framework incorporating patient-specific anatomy and electrode... Read more
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Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks
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... Read more
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Learning a Probabilistic Model for Diffeomorphic Registration
We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables... Read more
Articles, Published Articles
Laryngeal Pressure Estimation with a Recurrent Neural Network
    Abstract Quantifying the physical parameters of voice production is essential for understanding the process of phonation and can aid in voice research and diagnosis. As an alternative to invasive measurements, they can be estimated by formulating an inverse problem using a... Read more
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Position Paper on Computational Cardiology
   Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling and cardiovascular... Read more
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DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography
The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform... Read more
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simBCI – A framework for studying BCI methods by simulated EEG
Brain-Computer Interface (BCI) methods are commonly studied using Electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques real data is costly to obtain and its composition is apriori unknown. The brain mechanisms generating the... Read more
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Comparing Surface and Intramuscular Electromyography for Simultaneous and Proportional Control Based on a Musculoskeletal Model: A Pilot Study
     Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for... Read more