dr. B. Hunyadi

1. A Singular-value-based Map to Highlight Abnormal Regions Associated with Atrial Fibrillation Using High-resolution Electrograms and Multi-lead ECG
   H. Moghaddasi; R.C. Hendriks; B. Hunyadi; P. Knops; M.S. van Schie; N.M.S. de Groot; A.J. van der Veen;
   IEEE Trans. Biomedical Eng.,
   2024. DOI: 10.1109/TBME.2024.3420412
   document

2. Tensor decomposition-based data fusion for biomarker extraction from multiple EEG experiments
   Kenneth Stunnenberg; Richard Hendriks; Jantien Vroegop; Marloes Adank; Borbala Hunyadi;
   In International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
   2024.

3. Modeling nonlinear evoked hemodynamic responses in functional ultrasound
   S. Kotti; A. Erol; B. Hunyadi;
   In IEEE ICASSPW 2023 Workshop proceedings,
   2023.
   document

4. 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
   document

5. 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,
   February 2022.

6. 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.
   document

7. 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),
   September 2022.

8. Novel rank-based features of atrial potentials for the classification between paroxysmal and persistent atrial fibrillation
   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.
   document

9. 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;
   NeuroImage,
   Volume 228, pp. 117652, 2021. DOI: 10.1016/j.neuroimage.2020.117652

10. 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;
    Epilepsia,
    Volume 62, Issue 10, pp. 2333-2343, October 2021. DOI: https://doi.org/10.1111/epi.16990

11. 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.

12. 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
    document

13. 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;
    Epilepsia,
    Volume 61, Issue 4, pp. 766--775, 2020. DOI: 10.1111/epi.16470
    document

14. 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
    document

15. 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.
    document

16. 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,
    IEEE, 2020.

17. 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;
    Epilepsia Open,
    Volume 4, Issue 1, pp. 200-205, 2019. DOI: 10.1002/epi4.12294
    document

18. 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
    document

19. 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%.
    
    document

BibTeX support