AIMC Topic: Sleep Wake Disorders

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Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Apr 15, 2020
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associ...
Artificial Intelligence United States Humans Sleep Wake Disorders Academies and Institutes Polysomnography Sleep
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Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

International psychogeriatrics Mar 1, 2020
OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.
Middle Aged Disease Progression Male Cognitive Dysfunction Dementia Depression Anxiety Female Aged, 80 and over Aged Aggression Humans Behavioral Symptoms Machine Learning Delusions Neuropsychological Tests Sleep Wake Disorders Longitudinal Studies
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Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. ...
Sleep Stages Humans Sleep Wake Disorders Computer Simulation Polysomnography Deep Learning
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Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentio...
Sleep Wake Disorders Electrooculography Electroencephalography Polysomnography Diagnosis, Computer-Assisted Sleep Electromyography Deep Learning Sleep Stages Humans
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Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Current sleep medicine relies on the supervised analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an ...
Neural Networks, Computer Transfer, Psychology Databases, Factual Sleep Humans Electrooculography Sleep Stages Sleep Wake Disorders Electroencephalography Electromyography Deep Learning
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