Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation
Sleep specialists often conduct manual sleep stage scoring by visually inspecting the
patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult …
patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult …
SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging
Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …
at determining the label of individual target polysomnography epochs one at a time. In this …
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 …
A review on current trends in automatic sleep staging through bio-signal recordings and future challenges
Sleep staging is a vital process conducted in order to analyze polysomnographic data. To
facilitate prompt interpretation of these recordings, many automatic sleep staging methods …
facilitate prompt interpretation of these recordings, many automatic sleep staging methods …
ISRUC-Sleep: A comprehensive public dataset for sleep researchers
To facilitate the performance comparison of new methods for sleep patterns analysis,
datasets with quality content, publicly-available, are very important and useful. We introduce …
datasets with quality content, publicly-available, are very important and useful. We introduce …
Cross-subject zero calibration driver's drowsiness detection: Exploring spatiotemporal image encoding of EEG signals for convolutional neural network classification
This paper explores two methodologies for drowsiness detection using EEG signals in a
sustained-attention driving task considering pre-event time windows, and focusing on cross …
sustained-attention driving task considering pre-event time windows, and focusing on cross …
[HTML][HTML] Detection of REM sleep behaviour disorder by automated polysomnography analysis
N Cooray, F Andreotti, C Lo, M Symmonds… - Clinical …, 2019 - Elsevier
Abstract Objective Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour
Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully …
Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully …
SleepSEEG: automatic sleep scoring using intracranial EEG recordings only
N von Ellenrieder, L Peter-Derex… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. To perform automatic sleep scoring based only on intracranial
electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) …
electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) …
Automated classification of multi-class sleep stages classification using polysomnography signals: a nine-layer 1D-convolution neural network approach
SK Satapathy, D Loganathan - Multimedia Tools and Applications, 2023 - Springer
Sleep disorder diseases have one of the major health issues across the world. To handle
this issue the primary step taken by most of the sleep experts is the sleep staging …
this issue the primary step taken by most of the sleep experts is the sleep staging …
EEG sleep stages identification based on weighted undirected complex networks
Abstract Background and Objective Sleep scoring is important in sleep research because
any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings …
any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings …