dr. B. Hunyadi
Circuits and Systems (CAS), Department of Microelectronics
Expertise: Biomedical signal processingThemes: Health and Wellbeing
Borbála (Bori) Hunyadi was born in Budapest, Hungary. She received a MSc degree in electrical and computer engineering from the Pazmany Peter Catholic University in 2009. In the same year she joined Stadius, Department of Electrical Engineering at KU Leuven, where she worked in close collaboration with the Laboratory for Epilepsy Research, and she obtained her PhD degree in 2014. She continued working in Stadius as a postdoctoral researcher on the ERC advanced grant Biotensors, and she served as the research lead on the Imec-ICON project SeizeIT. Between February and May 2016 she was a visiting researcher at the University of Oxford, and in May 2017 she visited the Christian Albrechts University of Kiel. In 2018 she was awarded one of the “Delft Technology Fellowships” for outstanding female academic researchers. In October 2018 she joined the Circuits and Systems group at TU Delft as an assistant professor.
Her research interests include biomedical signal processing and machine learning for biomedical pattern recognition. More specifically, she is interested in multimodal signal processing and fusion, blind source separation, tensor decompositions and wearable signal processing to better understand healthy and pathological physiology. In particular, she is leading projects on ECG, EEG and (functional) ultrasound signal processing for a variety of applications including atrial fibrillation, epilepsy, neuroscience research and cancer detection.
She is the secretary of the IEEE EMBC Benelux chapter and vice-chair of the EURASIP technical area committee on biomedical signal and image processing.
EE2S31 Signal processing
Digital signal processing; stochastic processes
EE4530 Applied convex optimization
Applied convex optimization: role of convexity in optimization, convex sets and functions, Canonical convex problems (SDP, LP, QP), second-order methods, first-order methods for large-scale problems.
EE4750 Tensor networks for green AI and signal processing
Introduction to tensor linear algebra
Prostate cancer detection using ultrasound
Tensor techniques to improve the analysis of (3D+time) ultrasound images
Delft Tensor AI Lab
Tensor-based AI methods for biomedical signals
Multimodal, multiresolution brain imaging
Developing a novel brain imaging paradigm combining functional ultrasound and EEG
Medical Delta Cardiac Arrhythmia Lab
Part of a larger program (with Erasmus MC) to unravel and target electropathology related to atrial arrhythmia
- Classification of De Novo Post-Operative and Persistent Atrial Fibrillation Using Multi-Channel ECG Recordings
Hanie Moghaddasi; Richard C. Hendriks; Alle-Jan van der Veen; Natasja M.S. de Groot; Borbala Hunyadi;
Computers in Biology and Medicine,
Volume 143, April 2022. DOI: 10.1016/j.compbiomed.2022.105270
- Denoising of Dynamic Contrast-enhanced Ultrasound Sequences: a Multilinear Approach
Calis, Metin; Mischi, Massimo; van der Veen, Alle-Jan; Hunyadi, Borbala;
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies,
- Surface Electrocardiogram Reconstruction Using Intra-operative Electrograms
H. Moghaddasi; B. Hunyadi; A.J. van der Veen; N.M.S. de Groot; R.C. Hendriks;
In 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux (SITB 2022),
Louvain la Neuve, Belgium, pp. 136, 2022.
- Novel electrogram-based features for the classification between paroxysmal and persistent atrial fibrillation during sinus rhythm
H. Moghaddasi; R.C. Hendriks; A.J. van der Veen; N.M.S. de Groot; B. Hunyadi;
In 2022 Computing in Cardiology (CinC),
IEEE, September 2022.
- Multiparametric ultrasound and machine learning for prostate cancer localization
P. Chen; M. Calis; H. Wijkstra; P. Huang; B. Hunyadi; M. Mischi;
In 30th European Signal Processing Conference (EUSIPCO),
- Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data
Van Eyndhoven, Simon; Dupont, Patrick; Tousseyn, Simon; Vervliet, Nico; Van Paesschen, Wim; Van Huffel, Sabine; Hunyadi, Borbala;
Volume 228, pp. 117652, 2021. DOI: 10.1016/j.neuroimage.2020.117652
- The power of ECG in multimodal patient‐specific seizure monitoring: Added value to an EEG‐based detector using limited channels
Vandecasteele, Kaat; De Cooman, Thomas; Chatzichristos, Christos; Cleeren, Evy; Swinnen, Lauren; Macea Ortiz, Jaiver; Van Huffel, Sabine; Dumpelmann, Matthias; Schulze-Bonhage, Andreas; De Vos, Maarten; Van Patschen, Wim; Hunyadi, Borbala;
Volume 62, Issue 10, pp. 2333-2343, October 2021. DOI: https://doi.org/10.1111/epi.16990
- Tensors for neuroimaging: A review on applications of tensors to unravel the mysteries of the brain
Aybuke, Erol; Hunyadi, Borbala;
In Tensors for Data Processing: Theory, Methods, and Applications,
Elsevier, October 2021. eBook ISBN 9780323859653.
- Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning
Thomas De Cooman; Kaat Vandecasteele; Carolina Varon; Borbala Hunyadi; Evy Cleeren; Wim Van Paesschen; Sabine Van Huffel;
Frontiers in Neurology,
Volume 11, pp. 145, 2020. DOI: 10.3389/fneur.2020.00145
- Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels
Vandecasteele, Kaat; De Cooman, Thomas; Dan, Jonathan; Cleeren, Evy; Van Huffel, Sabine; Hunyadi, Borbala; Van Paesschen, Wim;
Volume 61, Issue 4, pp. 766--775, 2020. DOI: 10.1111/epi.16470
- Zebrafish-based screening of antiseizure plants used in Traditional Chinese Medicine: Magnolia officinalis extract and its constituents Magnolol and Honokiol exhibit potent anticonvulsant activity in a therapy-resistant epilepsy model
Li, Jing; Copmans, Danielle; Partoens, Michele; Hunyadi, Borbala; Luyten, Walter; de Witte, Peter;
ACS chemical neuroscience,
Volume 11, Issue 5, pp. 730--742, 2020. DOI: 10.1021/acschemneuro.9b00610
- Tensor-based Detection of Paroxysmal and Persistent Atrial Fibrillation from Multi-channel ECG
H. Moghaddasi; A.J. van der Veen; N.M.S. de Groot; B. Hunyadi;
In 29th European Signal Processing Conference (EUSIPCO 2020),
Amsterdam (Netherlands), EURASIP, pp. 1155-1159, August 2020.
- Joint Estimation of Hemodynamic Response and Stimulus Function in Functional Ultrasound Using Convolutive Mixtures
Aybuke Erol; Simon Van Eyndhoven; Sebastiaan Koekkoek; Pieter Kruizinga; Borbala Hunyadi;
In 2020 54th Asilomar Conference on Signals, Systems, and Computers,
- Development of temporal lobe epilepsy during maintenance electroconvulsive therapy: A case of human kindling?
C. Schotte; E. Cleeren; K. Goffin; B. Hunyadi; S. Buggenhout; K. Van Laere; W. Van Paesschen;
Volume 4, Issue 1, pp. 200-205, 2019. DOI: 10.1002/epi4.12294
- Semi-automated EEG enhancement improves localization of ictal onset zone with EEG-correlated fMRI
S. Van Eyndhoven; B. Hunyadi; P. Dupont; W. Van Paesschen; S. Van Huffel;
Frontiers in Neurology,
Volume 10, 2019. DOI: 10.3389/fneur.2019.00805
- Nonconvulsive epileptic seizure monitoring with incremental learning
Y.R. Rodriguez Aldana; E.J. Maranon Reyes; F. Sanabria Macias; V. Rodriguez Rodriguez; L. Morales Chacon; S. Van Huffel; B. Hunyadi;
Computers in Biology and Medicine,
Volume 114, pp. 103434, 2019. ISSN 0010-4825. DOI: 10.1016/j.compbiomed.2019.103434
Keywords: ...Nonconvulsive epileptic seizures, Hilbert huang transform, Multiway data analysis, Incremental learning.
Abstract: ...Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accurately detected NCSz in EEG data (referred here as ‘Batch method’). However, this approach was less effective when the EEG features identified at the beginning of the recording changed over time. Such pattern drift is an issue that causes failures of automated seizure detection methods. This paper presents a support vector machine (SVM)-based incremental learning method for NCSz detection that for the first time addresses the seizure evolution in EEG records from patients with epileptic disorders and from ICU having NCSz. To implement the incremental learning SVM, three methodologies are tested. These approaches differ in the way they reduce the set of potentially available support vectors that are used to build the decision function of the classifier. To evaluate the suitability of the three incremental learning approaches proposed here for NCSz detection, first, a comparative study between the three methods is performed. Secondly, the incremental learning approach with the best performance is compared with the Batch method and three other batch methods from the literature. From this comparison, the incremental learning method based on maximum relevance minimum redundancy (MRMR_IL) obtained the best results. MRMR_IL method proved to be an effective tool for NCSz detection in a real-time setting, achieving sensitivity and accuracy values above 99%.
Last updated: 28 Feb 2022
MSc project proposals
- Contactless determination of vital parameters for improved healthcare (with Intelliprove)
- Dynamic brain network analysis
- Accurate blood signal extraction to study brain function with functional ultrasound
- Optimizing the analysis of auditory event-related potentials in EEG (with Erasmus Medical Center)
- Energy-efficient seizure detection for wearable EEG