Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
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 …
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
S Chambon, MN Galtier, PJ Arnal… - … on Neural Systems …, 2018 - ieeexplore.ieee.org
Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
The future of sleep health: a data-driven revolution in sleep science and medicine
In recent years, there has been a significant expansion in the development and use of multi-
modal sensors and technologies to monitor physical activity, sleep and circadian rhythms …
modal sensors and technologies to monitor physical activity, sleep and circadian rhythms …
U-time: A fully convolutional network for time series segmentation applied to sleep staging
Neural networks are becoming more and more popular for the analysis of physiological time-
series. The most successful deep learning systems in this domain combine convolutional …
series. The most successful deep learning systems in this domain combine convolutional …
The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging
Abstract Study Objectives The development of ambulatory technologies capable of
monitoring brain activity during sleep longitudinally is critical for advancing sleep science …
monitoring brain activity during sleep longitudinally is critical for advancing sleep science …
Automated sleep scoring: A review of the latest approaches
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …
human expert, according to official standards. It could appear then a suitable task for modern …
[HTML][HTML] Machine-learning-based diagnostics of EEG pathology
LAW Gemein, RT Schirrmeister, P Chrabąszcz… - NeuroImage, 2020 - Elsevier
Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …