2016 - SIAM Fellow For bridging the gap between advanced numerical linear algebra techniques and biomedical signal processing.
2009 - IEEE Fellow For contributions to total least squares fitting and computational biosignal processing
Sabine Van Huffel focuses on Total least squares, Algorithm, Artificial intelligence, Electroencephalography and Singular value decomposition. Her Total least squares research includes themes of Estimation theory, Generalized least squares, Mathematical optimization and Applied mathematics. Sabine Van Huffel has researched Algorithm in several fields, including Time domain, Signal, Signal processing, Nonlinear system and Nuclear magnetic resonance.
Her work deals with themes such as Machine learning, Magnetic resonance imaging and Pattern recognition, which intersect with Artificial intelligence. Her work carried out in the field of Electroencephalography brings together such families of science as Artifact, Independent component analysis and Epilepsy. Her Singular value decomposition research includes elements of Linear prediction, Overdetermined system and Numerical linear algebra.
Her scientific interests lie mostly in Artificial intelligence, Pattern recognition, Electroencephalography, Algorithm and Internal medicine. She has included themes like Machine learning, Speech recognition and Computer vision in her Artificial intelligence study. Electroencephalography connects with themes related to Epilepsy in her study.
Her Algorithm study focuses mostly on Total least squares and Singular value decomposition. The Total least squares study combines topics in areas such as Generalized least squares, Numerical linear algebra, Applied mathematics and Non-linear least squares. Her Internal medicine study combines topics from a wide range of disciplines, such as Oncology and Cardiology.
Sabine Van Huffel mostly deals with Artificial intelligence, Pattern recognition, Electroencephalography, Internal medicine and Cardiology. Her studies in Artificial intelligence integrate themes in fields like Machine learning and Magnetic resonance imaging. The various areas that she examines in her Pattern recognition study include Tensor, White matter, Non-negative matrix factorization and Sensitivity.
Her Electroencephalography research is multidisciplinary, incorporating elements of Neurovascular coupling, Audiology, Speech recognition, Kappa and Epilepsy. Her Cardiology research is multidisciplinary, relying on both Heart rate variability, Blood pressure, Heart rate and Disease. Sabine Van Huffel combines subjects such as Respiratory system and Respiration with her study of Heart rate.
Sabine Van Huffel mainly investigates Artificial intelligence, Pattern recognition, Electroencephalography, Epilepsy and Magnetic resonance imaging. Her Artificial intelligence research integrates issues from Electrocardiography, Machine learning and Computer vision. Her work in Pattern recognition addresses subjects such as Random forest, which are connected to disciplines such as Test set.
Her Electroencephalography study combines topics in areas such as Audiology, Speech recognition, Kappa, Functional magnetic resonance imaging and Convolutional neural network. In her study, Respiration and Low complexity is inextricably linked to Heart rate, which falls within the broad field of Epilepsy. Her Smoothing research incorporates elements of Algorithm and Sleep.
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The Total Least Squares Problem: Computational Aspects and Analysis
Sabine Van Huffel;Joos Vandewalle.
Improved Method for Accurate and Efficient Quantification of MRS Data with Use of Prior Knowledge
Leentje Vanhamme;Aad van den Boogaart;Sabine Van Huffel.
Journal of Magnetic Resonance (1997)
Overview of total least-squares methods
Ivan Markovsky;Sabine Van Huffel.
Signal Processing (2007)
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
Unknown Journal (2018)
Antenatal Maternal Anxiety is Related to HPA-Axis Dysregulation and Self-Reported Depressive Symptoms in Adolescence: A Prospective Study on the Fetal Origins of Depressed Mood
Bea R H Van den Bergh;Ben Van Calster;Tim Smits;Sabine Van Huffel.
Review on solving the forward problem in EEG source analysis
Hans Hallez;Bart Vanrumste;Bart Vanrumste;Roberta Grech;Joseph Muscat.
Journal of Neuroengineering and Rehabilitation (2007)
Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group
Dirk Timmerman;Antonia Carla Testa;Tom Bourne;Enrico Ferrazzi.
Journal of Clinical Oncology (2005)
The total least squares problem
Sabine Van Huffel;H Zha.
Total Least Squares and Errors-in-Variables Modeling : Analysis, Algorithms and Applications
Sabine Huffel;Philippe Lemmerling.
The accuracy of transvaginal ultrasonography for the diagnosis of ectopic pregnancy prior to surgery
George Condous;Emeka Okaro;Asma Khalid;Chuan Lu.
Human Reproduction (2005)
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