Search Results for author: Maarten De Vos

Found 22 papers, 12 papers with code

SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification

no code implementations23 May 2021 Huy Phan, Kaare Mikkelsen, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Maarten De Vos

At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i. e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts.

EEG

Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring

no code implementations9 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.

Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones

1 code implementation16 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.

Activity Recognition Transfer Learning

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

1 code implementation24 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.

Federated Learning Time Series

Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation

2 code implementations21 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.

Change Point Detection Time Series

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

1 code implementation8 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.

Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers

1 code implementation4 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.

Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization

no code implementations23 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.

Transfer Learning

Improving GANs for Speech Enhancement

1 code implementation15 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.

Speech Enhancement

Screening for REM Sleep Behaviour Disorder with Minimal Sensors

1 code implementation24 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.

EEG

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

1 code implementation30 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.

Automatic Sleep Stage Classification Multimodal Sleep Stage Detection +2

Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch

no code implementations11 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.

Transfer Learning

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

1 code implementation12 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.

EEG

Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?

no code implementations2 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.

General Classification Scene Classification

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

2 code implementations28 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.

Classification General Classification +1

A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age

no code implementations19 Sep 2018 Kirubin Pillay, Maarten De Vos

Preterm newborns undergo various stresses that may materialize as learning problems at school-age.

EEG

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

1 code implementation16 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.

Automatic Sleep Stage Classification Classification +2

Personalizing deep learning models for automatic sleep staging

no code implementations8 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

Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization

no code implementations29 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.

EEG

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