Sleep Disorders

Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation

Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer from poor generalization to new subjects because they are trained and tested on the same labeled datasets, overlooking individual differences. To tackle this issue, we propose a novel Source-Free Unsupervised Individual ...

A novel deep learning model for obstructive sleep apnea diagnosis: hybrid CNN-Transformer approach for radar-based detection of apnea-hypopnea events.

STUDY OBJECTIVES: The demand for cost-effective and accessible alternatives to polysomnography (PSG), the conventional diagnostic method for obstructive sleep apnea (OSA), has surged. In this study, we have developed and validated a deep learning model for detecting apnea-hypopnea events using radar data.

EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences slee...

Improving snore detection under limited dataset through harmonic/percussive source separation and convolutional neural networks

Snoring, an acoustic biomarker commonly observed in individuals with Obstructive Sleep Apnoea Syndrome (OSAS), holds significant potential for diagn...

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method wit...

[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].

To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image rec...

Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models.

The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population because of limited access to polysomnograp...

Heterogeneous Effects of Continuous Positive Airway Pressure in Non-Sleepy Obstructive Sleep Apnea on Cardiovascular Disease Outcomes: Machine Learning Analysis of the ISAACC Trial (ECSACT Study).

Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with ob...

Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep.

This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We appli...

Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a disorder characterized by increased muscle tone and dream-enactment behaviors in REM sleep...

Predicting Sleep Quality via Unsupervised Learning of Cardiac Activity.

While highly important for a person's mood, productivity, and physical performance, perceived sleep quality is challenging to model and, thus, predict...

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a mino...

Evaluating insomnia queries from an artificial intelligence chatbot for patient education.

STUDY OBJECTIVES: We evaluated the accuracy of ChatGPT in addressing insomnia-related queries for patient education and assessed ChatGPT's ability to ...

Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.

STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for au...

A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (P...

Enhancing OSA Assessment with Explainable AI.

Explainable Artificial Intelligence (xAI) is a rapidly growing field that focuses on making deep learning models interpretable and understandable to h...

Identification of Sleep Patterns via Clustering of Hypnodensities.

Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashi...

[Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks].

Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomno...

Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings.

Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionab...

Classification of Sleep-Wake State in Ballistocardiogram system based on Deep Learning.

Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders...