activity
20172020
most citedNeural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy

424 citations · 557 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2020★ 95 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.CV2020★ 10 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…

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★ 10 cited

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★ 18 cited

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…

cs.NE2017★ 424 cited

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…