Sleep Disorders

[Discrimination of Chin Electromyography in REM Sleep Behavior Disorder Using Deep Learning].

OBJECTIVE: The confirmation of abnormal behavior during video monitoring in polysomnography (PSG) and the frequency of rapid eye movement (REM) sleep without atonia (RWA) during REM sleep based on physiological indicators are essential diagnostic criteria for the diagnosis of REM sleep behavior disorder (RBD). However, no clear criteria have been established for the determination of the tonic and ...

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea.

The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful a...

Application of Machine Learning to Sleep Stage Classification.

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often,...

The quantification and clinical analysis of depression and anxiety in patients undergoing Da Vinci robot-assisted radical gastrectomy and open radical gastrectomy.

The purpose of paper is to investigate the depression and anxiety as well as independent influential factors between patients who underwent Da Vinci r...

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may d...

Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standa...

Deep learning applied to polysomnography to predict blood pressure in obstructive sleep apnea and obesity hypoventilation: a proof-of-concept study.

STUDY OBJECTIVES: Nocturnal blood pressure (BP) profile shows characteristic abnormalities in OSA, namely acute postapnea BP surges and nondipping BP....

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample.

STUDY OBJECTIVES: Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. Th...

Deep transfer learning for improving single-EEG arousal detection.

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two...

Predicting Age with Deep Neural Networks from Polysomnograms.

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyz...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized ...

Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography si...

Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement.

Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial...

Artificial intelligence in sleep medicine: background and implications for clinicians.

Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is w...

Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models.

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topologic...

Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.

STUDY OBJECTIVES: Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nond...

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG s...

Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.

STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resul...

Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The ...

Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and...