AIMC Topic: Sleep Stages

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Classification of cyclic alternating patterns of sleep using EEG signals.

Sleep medicine Sep 24, 2024
Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP...
Adult Male Electroencephalography Machine Learning Algorithms Female Signal Processing, Computer-Assisted Polysomnography Sleep Stages Humans
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An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.

Computers in biology and medicine Sep 20, 2024
Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instrume...
Humans Female Adult Male Electromyography Deep Learning Polysomnography Electroencephalography Sleep Stages Signal Processing, Computer-Assisted
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PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

IEEE journal of biomedical and health informatics Sep 5, 2024
Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle hetero...
Algorithms Female Young Adult Humans Neural Networks, Computer Electroencephalography Electrooculography Adult Signal Processing, Computer-Assisted Sleep Stages Male Middle Aged
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SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging.

Computer methods and programs in biomedicine Sep 2, 2024
BACKGROUND AND OBJECTIVE: Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully e...
Sleep Stages Humans Neural Networks, Computer Machine Learning Algorithms Signal Processing, Computer-Assisted Electroencephalography Electrooculography
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MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.

Biosensors Aug 22, 2024
Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and re...
Electroencephalography Signal Processing, Computer-Assisted Electromyography Sleep Stages Animals Eye Movements Mice Neural Networks, Computer Machine Learning Algorithms
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Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

Journal of neuroscience methods Aug 14, 2024
BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wa...
Neural Networks, Computer Calcium Brain Attention Wakefulness Sleep Mice Animals Sleep Stages Mice, Inbred C57BL Male Electroencephalography Image Processing, Computer-Assisted
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Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 14, 2024
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance ...
Sleep Stages Deep Learning Female Neural Networks, Computer Humans Adult Machine Learning Electroencephalography Male Polysomnography Algorithms
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Automated remote sleep monitoring needs uncertainty quantification.

Journal of sleep research Aug 7, 2024
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...
Accelerometry Polysomnography Sleep Wake Disorders Uncertainty Sleep Stages Male Wearable Electronic Devices Electroencephalography Female Reproducibility of Results Aged Humans Machine Learning
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Common sleep data pipeline for combined data sets.

PloS one Aug 6, 2024
Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizin...
Humans Sleep Neural Networks, Computer Sleep Stages Machine Learning
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Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

Sleep & breathing = Schlaf & Atmung Jul 24, 2024
PURPOSE: The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achiev...
Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted Adult Polysomnography Male Sleep Stages Deep Learning
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