no code implementations • 19 Mar 2024 • Jingwei Zhang, Lauren Swinnen, Christos Chatzichristos, Victoria Broux, Renee Proost, Katrien Jansen, Benno Mahler, Nicolas Zabler, Nino Epitashvilli, Matthias Dümpelmann, Andreas Schulze-Bonhage, Elisabeth Schriewer, Ummahan Ermis, Stefan Wolking, Florian Linke, Yvonne Weber, Mkael Symmonds, Arjune Sen, Andrea Biondi, Mark P. Richardson, Abuhaiba Sulaiman I, Ana Isabel Silva, Francisco Sales, Gergely Vértes, Wim Van Paesschen, Maarten De Vos
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
no code implementations • 28 Jun 2023 • Joran Michiels, Maarten De Vos, Johan Suykens
Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.
no code implementations • 19 Jun 2023 • Joran Michiels, Maarten De Vos, Johan Suykens
In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data.
no code implementations • 7 Jun 2023 • Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos
As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount.
no code implementations • 27 Mar 2023 • Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan Suykens, Maarten De Vos
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Ranked #1 on Sleep Stage Detection on SHHS
no code implementations • 15 Feb 2023 • Navin Cooray, Zhenglin Li, Jinzhuo Wang, Christine Lo, Mahnaz Arvaneh, Mkael Symmonds, Michele Hu, Maarten De Vos, Lyudmila S Mihaylova
This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model.
1 code implementation • 9 Jan 2023 • Huy Phan, Kristian P. Lorenzen, Elisabeth Heremans, Oliver Y. Chén, Minh C. Tran, Philipp Koch, Alfred Mertins, Mathias Baumert, Kaare Mikkelsen, Maarten De Vos
In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose.
1 code implementation • 22 Sep 2022 • Nick Seeuws, Maarten De Vos, Alexander Bertrand
Methods: We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events.
no code implementations • 20 Sep 2022 • Oliver Y. Chén, Florian Lipsmeier, Huy Phan, Frank Dondelinger, Andrew Creagh, Christian Gossens, Michael Lindemann, Maarten De Vos
The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.
no code implementations • 29 Dec 2021 • Elisabeth R. M. Heremans, Huy Phan, Amir H. Ansari, Pascal Borzée, Bertien Buyse, Dries Testelmans, Maarten De Vos
This method consists of training a model with larger amounts of data from the source modality and few paired samples of source and target modality.
no code implementations • 23 May 2021 • Huy Phan, Kaare Mikkelsen, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Maarten De Vos
It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level.
no code implementations • 9 Apr 2021 • Kaare B. Mikkelsen, Huy Phan, Mike L. Rank, Martin C. Hemmsen, Maarten De Vos, Preben Kidmose
Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring.
1 code implementation • 16 Mar 2021 • Andrew P. Creagh, Florian Lipsmeier, Michael Lindemann, Maarten De Vos
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic.
1 code implementation • 24 Feb 2021 • Eoin Brophy, Maarten De Vos, Geraldine Boylan, Tomas Ward
To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
2 code implementations • 21 Aug 2020 • Tim De Ryck, Maarten De Vos, Alexander Bertrand
Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum.
1 code implementation • 8 Jul 2020 • Huy Phan, Oliver Y. Chén, Minh C. Tran, Philipp Koch, Alfred Mertins, Maarten De Vos
This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images.
Ranked #1 on Sleep Stage Detection on PhysioNet Challenge 2018
1 code implementation • 4 Jul 2020 • Oliver Carr, Fernando Andreotti, Kate E. A. Saunders, Niclas Palmius, Guy M. Goodwin, Maarten De Vos
The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder.
no code implementations • 23 Apr 2020 • Huy Phan, Kaare Mikkelsen, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Preben Kidmose, Maarten De Vos
We employ the pretrained SeqSleepNet (i. e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model.
2 code implementations • 15 Jan 2020 • Huy Phan, Ian V. McLoughlin, Lam Pham, Oliver Y. Chén, Philipp Koch, Maarten De Vos, Alfred Mertins
The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint.
1 code implementation • 24 Oct 2019 • Navin Cooray, Fernando Andreotti, Christine Lo, Mkael Symmonds, Michele T. M. Hu, Maarten De Vos
This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.
1 code implementation • 30 Jul 2019 • Huy Phan, Oliver Y. Chén, Philipp Koch, Zongqing Lu, Ian McLoughlin, Alfred Mertins, Maarten De Vos
We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.
Ranked #1 on Multimodal Sleep Stage Detection on Surrey-PSG
Automatic Sleep Stage Classification Multimodal Sleep Stage Detection +2
no code implementations • 11 Apr 2019 • Huy Phan, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Maarten De Vos
This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input.
no code implementations • 6 Apr 2019 • Huy Phan, Oliver Y. Chén, Lam Pham, Philipp Koch, Maarten De Vos, Ian McLoughlin, Alfred Mertins
Acoustic scenes are rich and redundant in their content.
1 code implementation • 12 Nov 2018 • Navin Cooray, Fernando Andreotti, Christine Lo, Mkael Symmonds, Michele T. M. Hu, Maarten De Vos
This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.
no code implementations • 2 Nov 2018 • Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task.
no code implementations • 2 Nov 2018 • Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
2 code implementations • 28 Sep 2018 • Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
no code implementations • 19 Sep 2018 • Kirubin Pillay, Maarten De Vos
Preterm newborns undergo various stresses that may materialize as learning problems at school-age.
1 code implementation • 16 May 2018 • Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos
While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways.
Ranked #2 on Sleep Stage Detection on MASS SS2
no code implementations • 8 Jan 2018 • Kaare Mikkelsen, Maarten De Vos
Starting from a general convolutional neural network architecture, we allow the model to learn individual characteristics of the first night of sleep in order to quantify sleep stages of the second night.
Neurons and Cognition
1 code implementation • 2017 Computing in Cardiology (CinC) 2017 • Fernando Andreotti, Oliver Carr, Marco A. F. Pimentel, Adam Mahdi, Maarten De Vos
Similarly, the convolutional neural network scored 72. 1% on the augmented database and 83% on the test set.
no code implementations • 29 Jun 2015 • Yipeng Liu, Maarten De Vos, Sabine Van Huffel
Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation.