AIMC Topic: Sleep Wake Disorders

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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.

Journal of medical Internet research May 6, 2025
BACKGROUND: Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artifi...
Wearable Electronic Devices Sleep Wake Disorders Humans Artificial Intelligence
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Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Feb 1, 2025
STUDY OBJECTIVES: Sleep disturbances lead to negative health outcomes and caregiver burden, particularly in community settings. This study aimed to investigate a predictive model for sleep efficiency and its associated features in older adults living...
Aged, 80 and over Male Dementia Actigraphy Female Activities of Daily Living Independent Living Aged Sleep Sleep Wake Disorders Humans Machine Learning
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Classification of Sleep-Wake State in Ballistocardiogram system based on 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, 2022
Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders. Polysomnography (PSG), the gold standard, is intrusive and inconvenient for regular/long-term slee...
Deep Learning Sleep Polysomnography Humans Sleep Wake Disorders Ballistocardiography
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Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionable insights. Algorithms that yield such representations ideally require as little a priori knowledge...
Learning Sleep Humans Sleep Wake Disorders Neural Networks, Computer Algorithms
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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...
Sleep Polysomnography Sleep Wake Disorders Artificial Intelligence United States Academies and Institutes Humans
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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.
Sleep Wake Disorders Aggression Behavioral Symptoms Delusions Dementia Longitudinal Studies Neuropsychological Tests Depression Cognitive Dysfunction Aged, 80 and over Male Disease Progression Machine Learning Middle Aged Anxiety Humans Female Aged
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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. ...
Humans Computer Simulation Polysomnography Sleep Wake Disorders Deep Learning Sleep Stages
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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...
Electrooculography Diagnosis, Computer-Assisted Deep Learning Sleep Stages Polysomnography Humans Sleep Wake Disorders Electromyography Electroencephalography Sleep
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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 ...
Electrooculography Sleep Stages Transfer, Psychology Humans Sleep Sleep Wake Disorders Deep Learning Electroencephalography Electromyography Neural Networks, Computer Databases, Factual
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