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 …
Transfer learning for non-image data in clinical research: a sco** review
A Ebbehoj, MØ Thunbo, OE Andersen… - PLOS Digital …, 2022 - journals.plos.org
Background Transfer learning is a form of machine learning where a pre-trained model
trained on a specific task is reused as a starting point and tailored to another task in a …
trained on a specific task is reused as a starting point and tailored to another task in a …
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 …
EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection
The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …
essential key to medical treatment. With the advances in deep learning, many approaches …
Multi-modal physiological signals based squeeze-and-excitation network with domain adversarial learning for sleep staging
Sleep staging is the basis of sleep medicine for diagnosing psychiatric and
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning
The availability of large amounts of data from continuous glucose monitoring (CGM),
together with the latest advances in deep learning techniques, have opened the door to a …
together with the latest advances in deep learning techniques, have opened the door to a …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
MetaPhys: few-shot adaptation for non-contact physiological measurement
There are large individual differences in physiological processes, making designing
personalized health sensing algorithms challenging. Existing machine learning systems …
personalized health sensing algorithms challenging. Existing machine learning systems …
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 …