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Found 7 papers, 2 papers with code
MSED: a multi-modal sleep event detection model for clinical sleep analysis
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.
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
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.
Deep transfer learning for improving single-EEG arousal detection
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics.
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
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.
Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness
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.
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals.
Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
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 technicians.
