424 citations · 638 across the 10 of their papers we have counts for
10 papers
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation
Pongpanut Osathitporn, Guntitat Sawadwuthikul, Punnawish Thuwajit +7
Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard ma…
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…
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…