Towards interpretable sleep stage classification using cross-modal transformers
Accurate sleep stage classification is significant for sleep health assessment. In recent
years, several machine-learning based sleep staging algorithms have been developed, and …
years, several machine-learning based sleep staging algorithms have been developed, and …
Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review
Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …
general, this task is very time-consuming for physicians to perform. Deep learning shows …
Automatic sleep stage classification using deep learning: signals, data representation, and neural networks
P Liu, W Qian, H Zhang, Y Zhu, Q Hong, Q Li… - Artificial Intelligence …, 2024 - Springer
In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep
assessment and sleep disorder diagnosis. However, traditional sleep stage classification …
assessment and sleep disorder diagnosis. However, traditional sleep stage classification …
Randomized Quaternion Minimal Gated Unit for sleep stage classification
BH Nuriye, H Seo, BS Oh - Expert Systems with Applications, 2024 - Elsevier
Automated sleep stage classification is imperative for detecting sleep-related disorders.
Previous studies predominantly favored single-channel sleep signals for their computational …
Previous studies predominantly favored single-channel sleep signals for their computational …
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Due to the abundant neurophysiological information in the electroencephalogram (EEG)
signal, EEG signals integrated with deep learning methods have gained substantial traction …
signal, EEG signals integrated with deep learning methods have gained substantial traction …
Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations
T Luo - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
As an important research branch of artificial intelligence, decoding motor imagery
electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing …
electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing …
Unravelling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification
CB Kumar, AK Mondal, M Bhatia, BK Panigrahi… - Applied Soft …, 2024 - Elsevier
Sleep data scoring is a crucial and primary step for diagnosing sleep disorders to know the
sleep stages from the PSG signals. This study uses supervised contrastive learning with a …
sleep stages from the PSG signals. This study uses supervised contrastive learning with a …
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities.
Sleep abnormalities can have severe health consequences. Automated sleep staging, ie
labelling the sequence of sleep stages from the patient's physiological recordings, could …
labelling the sequence of sleep stages from the patient's physiological recordings, could …
Multi-branch fusion graph neural network based on multi-head attention for childhood seizure detection
Y Li, Y Yang, S Song, H Wang, M Sun, X Liang… - Frontiers in …, 2024 - frontiersin.org
The most common manifestation of neurological disorders in children is the occurrence of
epileptic seizures. In this study, we propose a multi-branch graph convolutional network …
epileptic seizures. In this study, we propose a multi-branch graph convolutional network …
MHFNet: A Multimodal Hybrid-embedding Fusion Network for Automatic Sleep Staging
Scoring sleep stages is essential for evaluating the status of sleep continuity and
comprehending its structure. Despite previous attempts, automating sleep scoring remains …
comprehending its structure. Despite previous attempts, automating sleep scoring remains …