World's Best Scientists 2026 revealed!
Sabine Van Huffel

Sabine Van Huffel

Award Badge
Computer Science
Belgium
2026

D-Index & Metrics

Computer Science

D-Index
90
Citations
34811
World Ranking
606
National Ranking
6

Research.com Recognitions

  • 2026 - Research.com Computer Science in Belgium Leader Award
  • 2025 - Research.com Computer Science in Belgium Leader Award
  • 2022 - Research.com Computer Science in Belgium Leader Award
  • 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

Overview

Sabine Van Huffel is affiliated with KU Leuven in Belgium and has developed a significant body of research primarily in the fields of Medicine and Neuroscience. Their work spans multiple specialized subfields, including Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Computer Vision and Pattern Recognition, and Biomedical Engineering.

The research topics covered by Van Huffel include EEG and Brain-Computer Interfaces, Neonatal and fetal brain pathology, Non-Invasive Vital Sign Monitoring, Heart Rate Variability and Autonomic Control, ECG Monitoring and Analysis, Cardiac electrophysiology and arrhythmias, and Obstructive Sleep Apnea Research.

Among their recent papers are:

  • Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels, 2020, Epilepsia
  • Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty, 2022, Frontiers in Robotics and AI
  • The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels, 2021, Epilepsia
  • Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation, 2020, Sensors
  • icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions, 2021, NeuroImage Clinical

Van Huffel has frequently published in venues such as Computing in cardiology, Sensors, Physiological Measurement, arXiv (Cornell University), and Frontiers in Physiology.

The scientist has collaborated regularly with other researchers, including Carolina Varon, Gunnar Naulaers, Jonathan Moeyersons, Bertien Buyse, and Dries Testelmans.

Van Huffel was recognized as an SIAM Fellow in 2016 for contributions to bridging numerical linear algebra techniques and biomedical signal processing. Earlier, in 2009, they were named an IEEE Fellow for their work in total least squares fitting and computational biosignal processing.

Best Publications

  • 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

  • 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

  • Overview of total least-squares methods

    Ivan Markovsky;Sabine Van Huffel

  • Review on solving the forward problem in EEG source analysis

    Hans Hallez;Bart Vanrumste;Bart Vanrumste;Roberta Grech;Joseph Muscat

  • Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram

    Wim De Clercq;A. Vergult;B. Vanrumste;W. Van Paesschen

  • 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

  • Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group

    Dirk Timmerman;Lieveke Ameye;Daniela Fischerova;Elisabeth Epstein

  • Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

    Bogdan Mijović;M De Vos;I Gligorijević;J Taelman

  • The total least squares problem

    Sabine Van Huffel;H Zha

  • Triple-negative breast cancer: Present challenges and new perspectives

    Franca Podo;Lutgarde M.C. Buydens;Hadassa Degani;Riet Hilhorst

  • Total Least Squares and Errors-in-Variables Modeling : Analysis, Algorithms and Applications

    Sabine Huffel;Philippe Lemmerling

  • A tutorial on support vector machine-based methods for classification problems in chemometrics.

    Jan Luts;Fabian Ojeda;Raf Van de Plas;Bart De Moor

  • SLICOT—A Subroutine Library in Systems and Control Theory

    Peter Benner;Peter Benner;Volker Mehrmann;Vasile Sima;Sabine Van Huffel

  • A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG

    Carolina Varon;Alexander Caicedo;Dries Testelmans;Bertien Buyse

  • MR spectroscopy quantitation: a review of time‐domain methods

    Leentie Vanhamme;Tomas Sundin;Paul Van Hecke;Sabine Van Huffel

  • Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel

    German Gomez-Herrero;Wim Clercq;Haroon Anwar;Olga Kara

  • Analysis and properties of the generalized total least squares problem AX≈B when some or all columns in A are subject to error

    Sabine van Huffel;Joos Vandewalle

  • Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    Saurabh Jain;Diana M. Sima;Annemie Ribbens;Melissa Cambron

  • Brain tumor classification based on long echo proton MRS signals

    L. Lukas;A. Devos;J. A. K. Suykens;L. Vanhamme

  • Classification of brain tumours using short echo time 1H MR spectra.

    A. Devos;L. Lukas;J.A.K. Suykens;L. Vanhamme

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