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of 682
pro vyhledávání: '"De Vos, Maarten"'
Learning from multiple modalities, such as audio and video, offers opportunities for leveraging complementary information, enhancing robustness, and improving contextual understanding and performance. However, combining such modalities presents chall
Externí odkaz:
http://arxiv.org/abs/2405.07930
Autor:
Zhang, Jingwei, Swinnen, Lauren, Chatzichristos, Christos, Broux, Victoria, Proost, Renee, Jansen, Katrien, Mahler, Benno, Zabler, Nicolas, Epitashvilli, Nino, Dümpelmann, Matthias, Schulze-Bonhage, Andreas, Schriewer, Elisabeth, Ermis, Ummahan, Wolking, Stefan, Linke, Florian, Weber, Yvonne, Symmonds, Mkael, Sen, Arjune, Biondi, Andrea, Richardson, Mark P., Sulaiman I, Abuhaiba, Silva, Ana Isabel, Sales, Francisco, Vértes, Gergely, Van Paesschen, Wim, De Vos, Maarten
Objective: Most current wearable tonic-clonic seizure (TCS) detection systems are based on extra-cerebral signals, such as electromyography (EMG) or accelerometry (ACC). Although many of these devices show good sensitivity in seizure detection, their
Externí odkaz:
http://arxiv.org/abs/2403.13066
Model-agnostic feature attributions can provide local insights in complex ML models. If the explanation is correct, a domain expert can validate and trust the model's decision. However, if it contradicts the expert's knowledge, related work only corr
Externí odkaz:
http://arxiv.org/abs/2306.16431
Shapley values have become one of the go-to methods to explain complex models to end-users. They provide a model agnostic post-hoc explanation with foundations in game theory: what is the worth of a player (in machine learning, a feature value) in th
Externí odkaz:
http://arxiv.org/abs/2306.10880
Autor:
Heremans, Elisabeth R. M., Seedat, Nabeel, Buyse, Bertien, Testelmans, Dries, van der Schaar, Mihaela, De Vos, Maarten
As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount. A key component of reliability is the ability to estimate uncertainty, which
Externí odkaz:
http://arxiv.org/abs/2306.04663
The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly
Externí odkaz:
http://arxiv.org/abs/2304.06678
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could simplify the diagnostic process. Previous work on automated sleep staging
Externí odkaz:
http://arxiv.org/abs/2304.06485
Autor:
Cooray, Navin, Li, Zhenglin, Wang, Jinzhuo, Lo, Christine, Arvaneh, Mahnaz, Symmonds, Mkael, Hu, Michele, De Vos, Maarten, Mihaylova, Lyudmila S
Publikováno v:
2022 25th International Conference on Information Fusion (FUSION), Link\"oping, Sweden, 2022, pp. 01-08
Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious an
Externí odkaz:
http://arxiv.org/abs/2302.07509
Autor:
Phan, Huy, Lorenzen, Kristian P., Heremans, Elisabeth, Chén, Oliver Y., Tran, Minh C., Koch, Philipp, Mertins, Alfred, Baumert, Mathias, Mikkelsen, Kaare, De Vos, Maarten
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that w
Externí odkaz:
http://arxiv.org/abs/2301.03441
Objective: Finding events of interest is a common task in biomedical signal processing. The detection of epileptic seizures and signal artefacts are two key examples. Epoch-based classification is the typical machine learning framework to detect such
Externí odkaz:
http://arxiv.org/abs/2209.11007