AIMC Topic: Sleep

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Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Jun 1, 2021
STUDY OBJECTIVES: The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intellige...
Algorithms Reproducibility of Results Humans Artificial Intelligence Observer Variation Sleep Stages Electroencephalography Sleep
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Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Feb 1, 2021
STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multipl...
Reproducibility of Results Machine Learning Taiwan Algorithms Humans Sleep Artificial Intelligence Sleep Stages
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A deep learning-based algorithm for detection of cortical arousal during sleep.

Sleep Dec 14, 2020
STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep test...
Algorithms Deep Learning Polysomnography Arousal Electroencephalography Sleep
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Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.

Sleep Nov 12, 2020
STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.
Sleep Stages Sleep Scalp Humans Deep Learning Electroencephalography
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Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

Sleep Nov 12, 2020
STUDY OBJECTIVES: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term asse...
Sleep Sleep Stages Reproducibility of Results Deep Learning Photoplethysmography Sleep Apnea Syndromes Humans
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Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning.

Sleep Oct 13, 2020
STUDY OBJECTIVES: K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sen...
Humans Sleep Stages Sleep Polysomnography Deep Learning Electroencephalography
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Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population.

Sleep Sep 14, 2020
STUDY OBJECTIVES: To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.
Sleep Polysomnography Sleep Stages Algorithms Heart Rate Humans Neural Networks, Computer
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Sleep staging from electrocardiography and respiration with deep learning.

Sleep Jul 13, 2020
STUDY OBJECTIVES: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.
Deep Learning Respiration Sleep Stages Electrocardiography Sleep
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Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been us...
Sleep Stages Sleep Deep Learning Polysomnography Electroencephalography
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TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced addition...
Sleep Stages Sleep Humans Deep Learning Mental Recall Electroencephalography
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