Publications
Borbála Hunyadi
Publications at Google Scolar
Kim Batselier
Publications at Google Scolar
Journals
- Moghaddasi, H., Hendriks, R.C., Hunyadi, B., Knops, P., Van Schie, M.S., De Groot, N. and Van der Veen, A.-J. (2023). A Singular-value-based Marker for the Detection of Atrial Fibrillation Using High-resolution Electrograms and Multi-lead ECG. arXiv preprint arXiv. [https://arxiv.org/abs/2307.02806]
- Moghaddasi, H., Hendriks, R.C., Hunyadi, B., Knops, P., Van Schie, M.S., De Groot, N. and Van der Veen, A.-J. (2023). A Singular-value-based Marker for the Detection of Atrial Fibrillation Using High-resolution Electrograms and Multi-lead ECG. arXiv preprint arXiv. [https://arxiv.org/abs/2307.02806]
- Schuurmans, J.T., Batselier, K. and Kooij, J.F.P. (2023). How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?. arXiv preprint arXiv. [https://arxiv.org/abs/2305.05318]
- Moghaddasi, H., Hendriks, R.C., Van der Veen, A.-J., De Groot, N.M.S. and Hunyadi, B. (2022). Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings. Computers in Biology and Medicine. [https://www.sciencedirect.com/science/article/pii/S0010482522000622]
- Erol, A. and Hunyadi, B. (2022). Tensors for neuroimaging: A review on applications of tensors to unravel the mysteries of the brain. Tensors for Data Processing. [https://www.sciencedirect.com/science/article/abs/pii/B9780128244470000182]
- Erol, A., Soloukey, C., Generowicz, B., Van Dorp, N., Koekkoek, S., Kruizinga, P. and Hunyadi, B. (2022). Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition. Neuroinformatics. [https://link.springer.com/article/10.1007/s12021-022-09613-3]
- Erol, A., Soloukey, C., Generowicz, B., Van Dorp, N., Koekkoek, S., Kruizinga, P. and Hunyadi, B. (2022). Correction to: Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition. Neuroinformatics. [https://link.springer.com/article/10.1007/s12021-022-09619-x]
- Memmel, E., Menzen, C., Schuurmans, J., Wesel, F. and Batselier, K. (2022). Towards Green AI with tensor networks--Sustainability and innovation enabled by efficient algorithms. arXiv preprint arXiv. [https://arxiv.org/abs/2205.12961]
- Li, L., Yu, W., Batselier, K. (2022). Faster tensor train decomposition for sparse data. Journal of Computational and Applied Mathematics. [https://www.sciencedirect.com/science/article/abs/pii/S0377042721005720]
- Chen,C., Batselier, K., Yu, W. and Wong, N. (2022). Kernelized support tensor train machines. Pattern Recognition. [https://www.sciencedirect.com/science/article/abs/pii/S0031320321005173]
- Batselier, K. (2022). Low-rank tensor decompositions for nonlinear system identification: A tutorial with examples. IEEE Control Systems Magazine. [https://ieeexplore.ieee.org/abstract/document/9682625]
- Chen, C., Batselier, K. and Wong, N. (2022). Tensor network algorithms for image classification. Tensors for Data Processing. [https://www.sciencedirect.com/science/article/abs/pii/B9780128244470000145]
- Menzen, C., Kok, M. and Batselier, K. (2022). Alternating linear scheme in a Bayesian framework for low-rank tensor approximation. SIAM Journal on Scientific Computing. [https://epubs.siam.org/doi/abs/10.1137/20M1386414]
- Vandecasteele, K., De Cooman, T., Chatzichristos, C., Cleeren, E., Swinnen, L., Macea Ortiz, J., Van Huffel, S., Dümpelmann, M., Schulze?Bonhage, A., De Vos, M., Van Paesschen, W. and Hunyadi, B. (2021). The power of ECG in multimodal patient?specific seizure monitoring: added value to an EEG?based detector using limited channels. Epilepsia. [https://onlinelibrary.wiley.com/doi/full/10.1111/epi.16990]
- Van Eyndhoven, S., Dupont, P., Tousseyn, S., Vervliet, N., Van Paesschen, W., Van Huffel, S. and Hunyadi, B. (2021). Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. Neurolmage. [https://www.sciencedirect.com/science/article/pii/S105381192031137X]
- Wesel, F. and Batselier, K. (2021). Large-Scale Learning with Fourier Features and Tensor Decompositions. Advances in Neural Information Processing Systems. [https://proceedings.neurips.cc/paper_files/paper/2021/hash/92a08bf918f44ccd961477be30023da1-Abstract.html]
- Lucassen, M., Suykens, J.A.K. and Batselier, K. (2021). Tensor Network Kalman Filtering for Large-Scale LS-SVMs. arXiv preprint arXiv. [https://arxiv.org/abs/2110.13501]
- Batselier, K., Cichocki, A., Wong, N. (2021). Meracle: constructive layer-wise conversion of a tensor train into a mera. Communications on Applied Mathematics and Computation. [https://link.springer.com/article/10.1007/s42967-020-00090-6]
- Batselier, K. (2021). Enforcing symmetry in tensor network MIMO Volterra identification. IFAC-PapersOnLine. [https://www.sciencedirect.com/science/article/pii/S2405896321011782]
- Vandecasteele, K., De Cooman, T., Dan. J., Cleeren, E., Van Huffel, S., Hunyadi, B. and Van Paesschen, W. (2020). Visual seizure annotation and automated seizure detection using behind?the?ear electroencephalographic channels. Epilepsia. [https://onlinelibrary.wiley.com/doi/full/10.1111/epi.16470]
- De Cooman, T., Vandecasteele, K., Varon, C., Hunyadi, B., Cleeren, E., Van Paesschen, W. and Van Huffel, S. (2020). Personalizing heart rate-based seizure detection using supervised SVM transfer learning. Frontiers in Neurology. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=5OtqHeYAAAAJ&citation_for_view=5OtqHeYAAAAJ:isC4tDSrTZIC]
- Li, J., Copmans, D., Partoens, M., Hunyadi, B., Luyten, W. and De Witte, P. (2020). Zebrafish-Based Screening of Antiseizure Plants Used in Traditional Chinese Medicine: Magnolia officinalis Extract and Its Constituents Magnolol and Honokiol …. ACS Chemical Neuroscience. [https://pubs.acs.org/doi/abs/10.1021/acschemneuro.9b00610]
- Karagoz, R. and Batselier, K. (2020). Nonlinear system identification with regularized Tensor Network B-splines. Automatica. [https://www.sciencedirect.com/science/article/pii/S0005109820304994]
- Ko, C.-Y., Batselier, K., Daniel, L., Yu, W. and Wong, N. (2020). Fast and accurate tensor completion with total variation regularized tensor trains. IEEE Transactions on Image Processing. [https://ieeexplore.ieee.org/abstract/document/9098052]
Conferences
- Erol, A., Generowicz, B., Kruizinga, P. and Hunyadi, B. (2023). GLM-Regularized Low-Rank Factorization For Extracting Functional Response From Swept-3D Functional Ultrasound. ICASSP DSLW. [https://ieeexplore.ieee.org/abstract/document/10193574/]
- Kotti, S.-E., Erol, A. and Hunyadi, B. (2023). Modeling Nonlinear Evoked Hemodynamic Responses in Functional Ultrasound. ICASSP DSLW. [https://ieeexplore.ieee.org/abstract/document/10193541/]
- Wijnands., R., Dauwels, J., Serra, I., Kruizinga, P., Badura, A. and Hunyadi, B. (2023). Modeling and Inference of Sparse Neural Dynamic Functional Connectivity Networks Underlying Functional Ultrasound Data. ICASSP DSLW. [Modeling and Inference of Sparse Neural Dynamic Functional Connectivity Networks Underlying Functional Ultrasound Data]
- De Rooij, S.J.J., Batselier, K. and Hunyadi, B. (2023). Enabling Large-Scale Probabilistic Seizure Detection with a Tensor-Network Kalman Filter for LS-SVM. ICASSP EEG workshop. [https://scholar.google.com/citations?view_op=view_citation&hl=en&user=5OtqHeYAAAAJ&sortby=pubdate&citation_for_view=5OtqHeYAAAAJ:OU6Ihb5iCvQC]
- Wesel, F. and Batselier, K. (2023). Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data. International Conference on Artificial Intelligence and Statistics. [https://proceedings.mlr.press/v206/wesel23a.html]
- Moghaddasi, H., Hendriks, R.C., Van der Veen, A.-J., De Groot, N.M.S. and Hunyadi, B. (2022). Novel Rank-based Features of Atrial Potentials for the Classification Between Paroxysmal and Persistent Atrial Fibrillation. Computing in Cardiology (CinC). [https://ieeexplore.ieee.org/abstract/document/10081638]
- Calis, M., Mischi, M., Van Der Veen, A.-J. and Hunyadi, B. (2022). Denoising of Dynamic Contrast-enhanced Ultrasound Sequences: A Multilinear Approach. 15th International Joint Conference on Biomedical Engineering and Technologies (BIOSIGNALS). [https://www.researchgate.net/publication/358871945_Denoising_of_Dynamic_Contrast-enhanced_Ultrasound_Sequences_A_Multilinear_Approach]
- Chen, P., Calis, M., Wijkstra, H., Huang, P., Hunyadi, B. and Mischi, M. (2022). Multiparametric ultrasound and machine learning for prostate cancer localization. 30th European Signal Processing Conference (EUSIPCO). [https://ieeexplore.ieee.org/abstract/document/9909729]
- Moghaddasi, H., Hunyadi, B., Van der Veen, A.-J., De Groot, N.M.S. and Hendriks, R.C. (2022). Surface Electrocardiogram Reconstruction Using Intra-operative Electrograms. Pre-Proceedings of the 2022 Symposium on Information Theory and Signal Processing in the Benelux. [https://ris.utwente.nl/ws/portalfiles/portal/296423503/proceedings_SITB2022.pdf#page=143]
- De Rooij, S.J.S. and Hunyadi, B. (2022). Epileptic Seizure Detection using a Tensor-Network Kalman Filter for LS-SVMs. Pre-Proceedings of the 2022 Symposium on Information Theory and Signal Processing in the Benelux. [https://ris.utwente.nl/ws/portalfiles/portal/296423503/proceedings_SITB2022.pdf#page=59]
- Kotti, S.-E. andHunyadi, B. (2022). Tensor-based Hemodynamic Response Estimation in Functional Ultrasound Data. 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux (SITB). [https://research.tudelft.nl/en/publications/tensor-based-hemodynamic-response-estimation-in-functional-ultras]
- Moghaddasi Kelishomi, H., Hunyadi, B., Van der Veen,A.-J., De Groot, N.M.S. and Hendriks, R.C. (2022). Surface Electrocardiogram Reconstruction Using Intra-operative Electrograms. 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux (SITB). [https://repository.tudelft.nl/islandora/object/uuid:8eb0cbcb-e61e-4750-bbf4-169ceb2cbbab]
- Moghaddasi, H., Van der Veen, A.-J., De Groot, N.M.S. and Hunyadi, B. (2021). Tensor-based detection of paroxysmal and persistent atrial fibrillation from multi-channel ecg. 28th European Signal Processing Conference (EUSIPCO). [https://ieeexplore.ieee.org/abstract/document/9287718]
- Erol, A., Van Eyndhoven, S., Koekkoek, S., Kruizinga, P. and Hunyadi, B. (2020). Joint estimation of hemodynamic response and stimulus function in functional ultrasound using convolutive mixtures. 54th Asilomar Conference on Signals, Systems and Computers. [https://ieeexplore.ieee.org/abstract/document/9443299]
- Moghaddasi Kelishomi, H., Van der Veen, A.-J., De Groot, N.M.S., Hunyadi, B. (2020). Tensor-based Detection of Paroxysmal and Persistent Atrial Fibrillation from Multi-channel ECG. 28th European Signal Processing Conference (EUSIPCO). [https://repository.tudelft.nl/islandora/object/uuid:29d31e3d-702f-4fb6-991c-3cfb42f3bb95]
Others
- Vandecasteele, K., Van Huffel, S., Hunyadi, B. and Van Paesschen, W. (2021). Multimodal Epileptic Seizure Detection: Towards a Wearable Solution. KU Leuven. [https://www.esat.kuleuven.be/stadius/docs/events/20210419-MultimodalEpilepticSeizureDetectionTowardsAWearableSolution-KaatVandecasteele.pdf]
- Batselier, K. (2020). Data to reproduce experiments in research article "MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA". 4TU.ResearchData. [https://data.4tu.nl/articles/_/12763889/1]