615 citations · 627 across the 4 of their papers we have counts for
4 papers
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Alexander Neergaard Olesen, Stanislas Chambon, Valentin Thorey +3
Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterizatio…
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so…
A deep learning architecture to detect events in EEG signals during sleep
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as…
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Stanislas Chambon, Mathieu Galtier, Pierrick Arnal +2
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of…