Masksleepnet: A cross-modality adaptation neural network for heterogeneous signals processing in sleep staging
Deep learning methods have become an important tool for automatic sleep staging in recent
years. However, most of the existing deep learning-based approaches are sharply …
years. However, most of the existing deep learning-based approaches are sharply …
Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …
MS-HNN: Multi-scale hierarchical neural network with squeeze and excitation block for neonatal sleep staging using a single-channel EEG
Most existing neonatal sleep staging appro-aches applied multiple EEG channels to obtain
good performance. However, it potentially increased the computational complexity and led …
good performance. However, it potentially increased the computational complexity and led …
ProductGraphSleepNet: Sleep staging using product spatio-temporal graph learning with attentive temporal aggregation
The classification of sleep stages plays a crucial role in understanding and diagnosing sleep
pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert, which …
pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert, which …
Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction
Background and objective Graph neural networks (GNNs) are widely used for automatic
sleep staging. However, the majority of GNNs are based on spectral approaches, as far as …
sleep staging. However, the majority of GNNs are based on spectral approaches, as far as …
Towards real-time sleep stage prediction and online calibration based on architecturally switchable deep learning models
Despite the recent advances in automatic sleep staging, few studies have focused on real-
time sleep staging to promote the regulation of sleep or the intervention of sleep disorders …
time sleep staging to promote the regulation of sleep or the intervention of sleep disorders …
[HTML][HTML] The effect of coupled electroencephalography signals in electrooculography signals on sleep staging based on deep learning methods
The influence of the coupled electroencephalography (EEG) signal in electrooculography
(EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and …
(EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and …
Sleepvitransformer: Patch-based sleep spectrogram transformer for automatic sleep staging
L Peng, Y Ren, Z Luan, X Chen, X Yang… - … Signal Processing and …, 2023 - Elsevier
Sleep staging is a crucial aspect of sleep evaluation and disease diagnosis. Numerous
automatic schemes have been developed to replace the tedious and expensive task of …
automatic schemes have been developed to replace the tedious and expensive task of …
MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups
Deep learning-based automatic sleep staging methods have been widely applied for sleep
scoring and sleep diagnosis. However, most methods consider only a single temporal scale …
scoring and sleep diagnosis. However, most methods consider only a single temporal scale …
Group-level interpretation of electroencephalography signals using compact convolutional neural networks
H Joo, LDA Quan, D Kim, J Woo - IEEE Access, 2023 - ieeexplore.ieee.org
Despite the excellent performance of deep learning models for decoding
electroencephalography (EEG) signals, the lack of explainability hinders the implementation …
electroencephalography (EEG) signals, the lack of explainability hinders the implementation …