10 citations · 12 across the 4 of their papers we have counts for
6 papers
RobustSleepNet: Transfer learning for automated sleep staging at scale
Antoine Guillot, Valentin Thorey
Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice…
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Antoine Guillot, Fabien Sauvet, Emmanuel H During +1
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologist…
AI vs Humans for the diagnosis of sleep apnea
Valentin Thorey, Albert Bou Hernandez, Pierrick J. Arnal +1
Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these event…
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…