Sleep Disorders

Latest AI and machine learning research in sleep disorders for healthcare professionals.

2,783 articles

Neurology Subcategories: Autism Dementia Head Trauma Parkinson's Disease Seizures Sleep Disorders

Showing 127-147 of 2,783 articles

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomn...

May 2020 PMID: 32370185

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

OBJECTIVE: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic...

Jan 2020 PMID: 32062037

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an ...

Jan 2020 PMID: 31976919

Revisiting the value of polysomnographic data in insomnia: more than meets the eye.

BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this...

Dec 2019 PMID: 31978862

A hybrid self-attention deep learning framework for multivariate sleep stage classification.

BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patte...

Dec 2019 PMID: 31787093

A Systematic Review of Detecting Sleep Apnea Using Deep Learning.

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, ...

Nov 2019 PMID: 31726771

Sleep stage classification from heart-rate variability using long short-term memory neural networks.

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and...

Oct 2019 PMID: 31578345

Gait can reveal sleep quality with machine learning models.

Sleep quality is an important health indicator, and the current measurements of sleep rely on questi...

Sep 2019 PMID: 31553783

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series.

OBJECTIVE: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to d...

Sep 2019 PMID: 31349239

A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

Automatic sleep staging methods usually extract hand-crafted features or network trained features fr...

Aug 2019 PMID: 31478877

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

A neural network model was previously developed to predict melatonin rhythms accurately from blue li...

Jul 2019 PMID: 31358781

Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.

BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accu...

Jun 2019 PMID: 31416548

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functionin...

May 2019 PMID: 31139933

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

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. Howeve...

Nov 2018 PMID: 30421469

Neurology

Sleep Disorders

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Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.

Revisiting the value of polysomnographic data in insomnia: more than meets the eye.

A hybrid self-attention deep learning framework for multivariate sleep stage classification.

A Systematic Review of Detecting Sleep Apnea Using Deep Learning.

Sleep stage classification from heart-rate variability using long short-term memory neural networks.

Gait can reveal sleep quality with machine learning models.

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series.

A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A RR interval based automated apnea detection approach using residual network.

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

Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks.

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

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

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