Automatic sleep staging of EEG signals: recent development, challenges, and future directions
Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
A review of automated sleep stage based on EEG signals
X Zhang, X Zhang, Q Huang, Y Lv, F Chen - Biocybernetics and Biomedical …, 2024 - Elsevier
Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep
stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging …
stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging …
A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel EEG
Background and objective: This paper presents a new framework for automatic classification
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …
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 …
EEG-ConvTransformer for single-trial EEG-based visual stimulus classification
Different categories of visual stimuli evoke distinct activation patterns in the human brain.
These patterns can be captured with EEG for utilization in application such as Brain …
These patterns can be captured with EEG for utilization in application such as Brain …
MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning
N Banluesombatkul, P Ouppaphan… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of
skilled clinicians. Deep learning approaches have been introduced in order to challenge the …
skilled clinicians. Deep learning approaches have been introduced in order to challenge the …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG
C Zhao, J Li, Y Guo - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objective: Single-channel EEG is the most popular choice of sensing
modality in sleep staging studies, because it widely conforms to the sleep staging …
modality in sleep staging studies, because it widely conforms to the sleep staging …
CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets
Sleep relaxes and rests the body by slowing down the metabolism, making us physically
stronger and fitter when we wake up. However, in a sleep disorder that may occur in …
stronger and fitter when we wake up. However, in a sleep disorder that may occur in …