[HTML][HTML] Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Automated sleep scoring: A review of the latest approaches
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …
human expert, according to official standards. It could appear then a suitable task for modern …
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
[HTML][HTML] Quantitative evaluation of EEG-biomarkers for prediction of sleep stages
Electroencephalography (EEG) is immediate and sensitive to neurological changes
resulting from sleep stages and is considered a computing tool for understanding the …
resulting from sleep stages and is considered a computing tool for understanding the …
Joint classification and prediction CNN framework for automatic sleep stage classification
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …
This paper proposes a joint classification-and-prediction framework based on convolutional …
[HTML][HTML] A systematic review of sensing technologies for wearable sleep staging
SA Imtiaz - Sensors, 2021 - mdpi.com
Designing wearable systems for sleep detection and staging is extremely challenging due to
the numerous constraints associated with sensing, usability, accuracy, and regulatory …
the numerous constraints associated with sensing, usability, accuracy, and regulatory …
[HTML][HTML] Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
Abstract Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy
(T1N) currently requires visual inspection of polysomnography records by trained scoring …
(T1N) currently requires visual inspection of polysomnography records by trained scoring …
A review of automated sleep disorder detection
Automated sleep disorder detection is challenging because physiological symptoms can
vary widely. These variations make it difficult to create effective sleep disorder detection …
vary widely. These variations make it difficult to create effective sleep disorder detection …
Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network
P Jadhav, G Rajguru, D Datta… - Biocybernetics and …, 2020 - Elsevier
For automatic sleep stage classification, the existing methods mostly rely on hand-crafted
features selected from polysomnographic records. In this paper, the goal is to develop a …
features selected from polysomnographic records. In this paper, the goal is to develop a …
An improved neural network based on SENet for sleep stage classification
Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by
psychologists is time-consuming. In this paper, we propose an automatic sleep staging …
psychologists is time-consuming. In this paper, we propose an automatic sleep staging …