Sleep Disorders

Longitudinal effects of CPAP therapy on MRI-Perivascular spaces in obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a disorder marked by repeated episodes of airway collapse during sleep, leading to hypoxaemia and sympathoexcitation. Its impact on brain fluid transport remains unclear. We investigated MRI-visible perivascular spaces (PVS) in healthy controls (n=20; 5 females, mean{+/-}SD age = 52.1{+/-}9.9 years) and OSA patients (n=20; 3 females, mean{+/-}SD age = 54.6{+/-}9.6...

KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry

Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through polysomnography is resource-intensive and limits widespread access, creating a critical need for accurate and efficient alternatives. In this paper, we introduce KindSleep, a deep learning framework that integrates clinical ...

ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring

Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced the field, ma...

Local REM sleep-N1-wake sleep stage mixing in narcolepsy type 1

Type 1 narcolepsy (NT1), a disorder caused by the loss of hypocretin/orexin transmission, is characterized by daytime sleepiness and symptoms where Ra...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...

Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder

Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagno...

Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder

Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagno...

Early Detection of Absurdity Signals in Pharmacovigilance: A Machine Learning Ensemble Approach to Identify Rare Adverse Drug Reactions

Background: Traditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially miss...

AcceleRest: A Physiology-Aware Masked Autoencoder for Wrist Accelerometer-based Sleep Staging and Apnea Evaluation

Sleep is essential for physical and mental health, yet large-scale assessment of sleep stages and sleep apnea is limited by the cost and burden of cli...

SleePyPhases: A Workflow Framework for Sleep Data Harmonization, Analysis and Machine Learning

Data-driven sleep research relies on polysomnography data from various public repositories and vendor systems. Yet the lack of standardized access met...

Artificial intelligence or sleep experts: comparing polysomnographic sleep staging in children and adolescents.

STUDY OBJECTIVES: The manual annotation of polysomnography (PSG) hypnograms is difficult and time-consuming. U-Sleep is an alternative, fast, and publ...

eegFloss: A Python package for refining sleep EEG recordings using machine learning models

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their...

Deciphering the role of nicotinamide metabolism and melanin-related genes in acute myocardial infarction: a machine learning approach integrating bioinformatics analysis.

Acute myocardial infarction (AMI) represents a significant global mortality factor. Alterations in nicotinamide metabolism within the myocardium post-...

Ultra-low-power System-on-Chip for automated screening of central apnea and hypopnea via chin electromyography.

Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading...

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes.

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and acc...

Sleep Stage Classification using Multimodal Embedding Fusion from EOG and PSM

Accurate sleep stage classification is essential for diagnosing sleep disorders, particularly in aging populations. While traditional polysomnograph...

Altered cerebral functional activity and its associated genetic profiles underlying chronic insomnia disorder before and after treatment.

OBJECTIVES: The resting-state cerebral functional activity underlying chronic insomnia disorder (CID) remains inconsistent, and the effects of pharmac...

A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.

UNLABELLED: Generative artificial intelligence utilizing transformer technology is widely seen as a groundbreaking advancement in applied artificial i...

Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose ...

Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis.

Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography...