no code implementations • 7 Jan 2021 • Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
F1 scores for the optimized joint detection model were 0. 70, 0. 63, and 0. 62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0. 65, 0. 61, and 0. 60 for the optimized single-event models.
1 code implementation • 21 Aug 2020 • Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts.
no code implementations • 10 Apr 2020 • Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics.
no code implementations • 16 May 2019 • Alexander Neergaard Olesen, Stanislas Chambon, Valentin Thorey, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
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 characterization of sleep patterns and possible diagnosis of sleep disorders.
no code implementations • 15 Mar 2019 • Andreas Brink-Kjaer, Alexander Neergaard Olesen, Paul E. Peppard, Katie L. Stone, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
In a dataset of 1, 026 PSGs, the MAD achieved a F1 score of 0. 76 for arousal detection, while wakefulness was predicted with an accuracy of 0. 95.
no code implementations • 8 Oct 2018 • Alexander Neergaard Olesen, Poul Jennum, Paul Peppard, Emmanuel Mignot, Helge Bjarup Dissing Sorensen
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals.