AIMC Topic: Electrooculography

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2024
Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep st...
Male Deep Learning Young Adult Adult Polysomnography Electrooculography Humans Sleep Stages Electroencephalography Signal Processing, Computer-Assisted Algorithms Neural Networks, Computer Female
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Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep Oct 21, 2019
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. ...
Neural Networks, Computer Reproducibility of Results Deep Learning Humans Sleep Stages Polysomnography Electroencephalography Electrooculography Electromyography
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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 Sleep Stages Electroencephalography Electromyography Diagnosis, Computer-Assisted Polysomnography Sleep Wake Disorders Deep Learning Humans 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 Sleep Sleep Wake Disorders Transfer, Psychology Databases, Factual Deep Learning Humans Electroencephalography Neural Networks, Computer Electromyography
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A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 1, 2018
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signa...
Electromyography Expert Systems Polysomnography Electrooculography Multivariate Analysis Humans Electroencephalography Algorithms Signal Processing, Computer-Assisted Sleep Stages Deep Learning Computer Systems Decision Trees
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An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand.

Biomedizinische Technik. Biomedical engineering Jun 1, 2015
The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable...
Movement Exoskeleton Device Hand Brain-Computer Interfaces Computer Systems Electrooculography Adult Humans Robotics Electroencephalography Paralysis Male Brain Signal Processing, Computer-Assisted
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