424 citations · 654 across the 8 of their papers we have counts for
8 papers
MSED: a multi-modal sleep event detection model for clinical sleep analysis
Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot +1
Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual sc…
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot +1
Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability.…
Deep transfer learning for improving single-EEG arousal detection
Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot +1
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strate…
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner +2
Neural networks are becoming more and more popular for the analysis of physiological time-series. The most successful deep learning systems in this domain combine convolutional and…
Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness
Andreas Brink-Kjaer, Alexander Neergaard Olesen, Paul E. Peppard +4
Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human…
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…
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
Alexander Neergaard Olesen, Poul Jennum, Paul Peppard +2
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50…
Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
Jens B. Stephansen, Alexander N. Olesen, Mads Olsen +27
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technician…