AIMC Topic: Sleep Wake Disorders

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Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, ...

Automated sleep scoring: A review of the latest approaches.

Sleep medicine reviews
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 artificial intelligence algorithms. Indeed, machine learning algor...

Using heart rate profiles during sleep as a biomarker of depression.

BMC psychiatry
BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart ...

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.

PloS one
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 experts and is limited by inter-rater reliability. In this paper, we propo...

A deep learning-based decision support system for diagnosis of OSAS using PTT signals.

Medical hypotheses
Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause ...

Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: An exploratory machine-learning study.

Human psychopharmacology
OBJECTIVE: Features of posttraumatic stress disorder (PTSD) typically include sleep disturbances, impaired declarative memory, and hyperarousal. This study evaluated whether these combined features may accurately delineate pathophysiological changes ...

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.

International journal of environmental research and public health
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environme...

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Journal of sleep research
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here...

Prevalence of and factors related to anxiety and depression symptoms among married patients with gynecological malignancies in China.

Asian journal of psychiatry
OBJECTIVE: This study aims to investigate the prevalence of anxiety and depression among married patients with gynecological malignancies in China and then explores factors related to anxiety and depression.

Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources...