Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
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 …
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 …
Sleeptransformer: Automatic sleep staging with interpretability and uncertainty quantification
Background: Black-box skepticism is one of the main hindrances impeding deep-learning-
based automatic sleep scoring from being used in clinical environments. Methods: Towards …
based automatic sleep scoring from being used in clinical environments. Methods: Towards …
Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification
Sleep stage classification is essential for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high classification …
Although previous attempts to classify sleep stages have achieved high classification …
XSleepNet: Multi-view sequential model for automatic sleep staging
Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …
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 …
[PDF][PDF] GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification.
Sleep stage classification is essential for sleep assessment and disease diagnosis.
However, how to effectively utilize brain spatial features and transition information among …
However, how to effectively utilize brain spatial features and transition information among …
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 …
Deep learning for predicting respiratory rate from biosignals
In the past decade, deep learning models have been applied to bio-sensors used in a body
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …