Sleep Disorders

A Single-Channel EEG Approach for Sleep Stage-Independent Automatic Detection of REM Sleep Behavior Disorder

Rapid Eye Movement (REM) Sleep Behavior Disorder (RBD) is a parasomnia characterized by the loss of physiological muscle atonia during REM sleep, often manifesting through dream-enacting behavior. Idiopathic RBD is largely considered a prodromal stage of neurodegenerative diseases, with a conversion rate to overt α-synucleinopathies of up to 96% after 14 years. Currently, the diagnostic procedure ...

Automated Insomnia Phenotyping from Electronic Health Records: Leveraging Large Language Models to Decode Clinical Narratives

Insomnia is a highly prevalent but often underdiagnosed condition in clinical practice. Its inconsistent documentation in electronic health records (EHRs) limits population-level analyses and obstructs efforts to evaluate treatment patterns or outcomes. We present a novel, fully automated approach for phenotyping insomnia directly from unstructured clinical notes using generative large language mo...

Foundation Models Reveal Untapped Health Information in Human Polysomnographic Sleep Data

Traditional sleep assessment methods rely on visual scoring of polysomnography (PSG), categorizing sleep into discrete stages (Wake, N1, N2, N3, REM) ...

Neuroinflammation distinguishes HLA haplotypes in progressive supranuclear palsy

Progressive supranuclear palsy (PSP) is a neurodegenerative 4R tauopathy clinically presenting with atypical parkinsonism or cognitive behavioral chan...

Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home

There is a growing interest in performing automated, longitudinal tracking of sleep in the home environment using wearables and machine learning. Wear...

Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts

Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensiona...

Automatic Sleep Staging from CPAP Airflow using a Dual Fusion Multi-Period Convolutional Neural Network

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea-hypopnea syndrome, yet its use as...

The Brain Imaging and Neurophysiology Database: BINDing multimodal neural data into a large-scale repository

The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositorie...

Optimized Machine Learning Algorithms for the Classification and Diagnosis of Sleep Disorders

Sleep disorders, including insomnia and obstructive sleep apnea, affect millions of individuals worldwide but are frequently undetected due to the hig...

Bedtime Brain State Predicts the Impact of Closed-Loop Auditory Stimulation on Sleep and Cognition

Sleep interventions targeting slow-wave activity (SWA) show heterogeneous effects across individuals. We investigated whether pre-sleep brain states p...

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to Wakefulness

To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...

A Zero-Burden Sleep Foundation Model Built on Cardiorespiratory Signals from 800,000+ Hours of Multi-Ethnic Sleep Recordings

Sleep disorders pose a major global health burden and are associated with a wide range of adverse health outcomes. Polysomnography (PSG) is the gold s...

Automated Sleep Stage and Event Detection Algorithms Using Quality-Controlled PSG Annotations

To develop machine-learning models for sleep stage classification, arousal detection, and respiratory event detection from polysomnography (PSG), and ...

Temporally Continuous Automated Sleep-Wake Classification Using Deep Learning

Segmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accuratel...

SleepGPT: A Sleep Stage Language Model for Efficient Sleep Assessment

Accurate and scalable sleep assessment is crucial for diagnosing sleep disorders and advancing personalized medicine. However, current approaches heav...

Research hotspots and future trends of insomnia in Parkinson's disease: a bibliometric and visualization analysis from 1973 to 2024.

UNLABELLED: Despite the growing body of research on Parkinson's disease (PD) and insomnia, comprehensive analysis of overall research trends remains ...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-section...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and th...

An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disr...

Mamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography

Study Objectives: We investigate Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non...