The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications

By C. Iber, S. Ancoli-Isreal, A. Chesson, S. Quan. 2007

5,916 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1d53bd4644546432865e391b02fa990af8239c64

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Citations in the corpus

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, methodology): “The ﬁrst is that hand-engineering of features based on the AASM manual (SAE model) may have better performance than automatic ﬁlter learning (CNN model), although the diﬀerence based on the data set we used does not appear to be statistically signiﬁcant.”
Processing of emotional reactivity and emotional memory over sleep (2012, unknown): “Data were obtained and analyzed according to the specifications provided in the revised AASM manual (Iber et al., 2007).”
Exploring the physiological, neurophysiological and cognitive performance effects of elevated carbon dioxide concentrations indoors (2019, background): “EEG is not yet a feature of research assessing the effects of indoor-realistic concentrations of CO 2 upon human performance, despite its use to measure neurological arousal at very high concentrations of CO 2 [13,17,18], and its use in neuroscience to objectively characterise sleepiness [25,26].”
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, methodology): “ing continuity of sleep stages and strong dependency between consecutive epochs [33]–[35].”
About sleep's role in memory. (2013, background): “Non-REM sleep includes slow-wave sleep (SWS) corresponding to N3, and lighter sleep stages N1 and N2 (591).”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, background): “During sleep our brain goes through a series of changes between different sleep stages, which are characterized by specific brain and body activity patterns [Kales and Rechtschaffen, 1968, Iber and AASM, 2007].”
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders (2015, methodology): “The AASM manual [11] includes a number of rules that recommend taking into account neighbouring epochs for the scoring of each current epoch under certain circum-stances.”
Wearable Sleep Technology in Clinical and Research Settings (2019, methodology): “A common issue in performing EBE analysis is that wearables devices do not always provide 30-s sleep scoring data, which should be the ideal recording time to match with standard PSG scoring (8).”
Short- and long-term health consequences of sleep disruption (2017, unknown): “A newer sleep classification system developed by the American Academy of Sleep Medicine has only three stages of NREM sleep: lighter sleep (Stages N1 and N2) and deeper sleep (or SWS; Stage N3).(10) The major changes with the newer classification system are focused on electroencephalogram (EEG) deri”
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, methodology): “Inspired by the way a sleep expert performs manual scoring [5], interpretability in automatic sleep scoring is reasonably about (but not limited to) what features the model learns from the input signal, whether these features are relevant to and underpin the sleep stages, and how the decision on a t”
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, methodology): “Manual scoring was done by sleep experts according to the AASM guideline [7].”
Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation Scientific Statement from the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Coun (2008, methodology): “The importance of the cardiovascular response to sleep has been recognized in the recently revised Sleep Scoring Manual from the American Association of Sleep Medicine (AASM) (33), which now includes scoring of a continuous-lead ECG as a recommended component of polysomnography (34).”