activity
20182020
most citedAutomatic sleep stage classification with deep residual networks in a mixed-cohort setting

95 citations · 166 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV202095 cited

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.…

cs.CV202010 cited

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…

cs.LG201961 cited

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…

q-bio.NC2019

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…

eess.SP2019

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…

cs.CV2018

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…