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Sleep Stages

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Detection and location of EEG events using deep learning visual inspection.

PloS one
The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject's health state in relation to ...

Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.

Neurocase
Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain ac...

A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

International journal of neural systems
Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. Howe...

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Computers in biology and medicine
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencep...

Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Sensors (Basel, Switzerland)
Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as i...

Latent alignment in deep learning models for EEG decoding.

Journal of neural engineering
. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning ...

S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models.

Computers in biology and medicine
Machine-learning-based automatic sleep stage scoring is a promising approach to enhance the time-consuming manual annotation process of polysomnography recordings. Although numerous algorithms have been proposed for this purpose, systematic explorati...

PhysioEx: a new Python library for explainable sleep staging through deep learning.

Physiological measurement
Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx (Physiological Signal Explainer), a Python library designed to support the analysis of sleep stages usin...

GraphSleepFormer: a multi-modal graph neural network for sleep staging in OSA patients.

Journal of neural engineering
Obstructive sleep apnea (OSA) is a prevalent sleep disorder. Accurate sleep staging is one of the prerequisites in the study of sleep-related disorders and the evaluation of sleep quality. We introduce a novel GraphSleepFormer (GSF) network designed ...

Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.

Journal of neural engineering
Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilit...