Automatic sleep staging of EEG signals: recent development, challenges, and future directions
H Phan, K Mikkelsen - Physiological Measurement, 2022 - iopscience.iop.org
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 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 …
Current status and prospects of automatic sleep stages scoring
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual
procedure requires considerable human and financial resources, and incorporates some …
procedure requires considerable human and financial resources, and incorporates some …
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
S Bagchi, DR Bathula - Pattern Recognition, 2022 - Elsevier
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 …