Sleep Disorders

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based...

Cross-Gram matrices and their use in transfer learning: Application to automatic REM detection using heart rate.

BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years to relieve sleep staging from its heavy requirements, through the collection of more easily assessable signals and its automation using machine learnin...

Machine learning-based preoperative datamining can predict the therapeutic outcome of sleep surgery in OSA subjects.

Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep ...

AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning.

Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance duri...

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data.

The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time...

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.

Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep sta...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for ...

Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder.

We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional m...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical condit...

A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI.

PURPOSE: The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatme...

Screening of sleep apnea based on heart rate variability and long short-term memory.

PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of ...

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of ...

Detection of Snore from OSAHS Patients Based on Deep Learning.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunct...

Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor.

Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold s...

Automatic sleep scoring: A deep learning architecture for multi-modality time series.

BACKGROUND: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the ...

Sleep stage classification for child patients using DeConvolutional Neural Network.

Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort ha...

Photoplethysmographic-based automated sleep-wake classification using a support vector machine.

OBJECTIVE: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importa...

Greedy based convolutional neural network optimization for detecting apnea.

BACKGROUND AND OBJECTIVE: Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test call...

Predicting polysomnographic severity thresholds in children using machine learning.

BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breath...

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. Polysomnography (PSG) has long been used for detection of ...