AIMC Topic: Sleep Wake Disorders

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Automated sleep staging model for older adults based on CWT and deep learning.

Scientific reports
Sleep staging plays a crucial role in the diagnosis and treatment of sleep disorders. Traditional sleep staging requires manual classification by professional technicians based on the characteristic features of each sleep stage. This process is time-...

A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.

Proceedings of the National Academy of Sciences of the United States of America
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BMC public health
BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in colleges. While existing research has indicated that the pandemic has had a significant impact on sleep...

SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator.

Computers in biology and medicine
Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened questionn...

Artificial Intelligence Can Drive Sleep Medicine.

Sleep medicine clinics
This article explores the transformative role of artificial intelligence (AI) in sleep medicine, highlighting its applications in detecting sleep microstructure patterns and integrating novel metrics. AI enhances diagnostic accuracy and objectivity, ...

Exploring sensory alterations and repetitive behaviors in children with autism spectrum disorder from the perspective of artificial neural networks.

Research in developmental disabilities
BACKGROUND: Restrictive repetitive behaviors (RRBs) and sensory processing disorders are core symptoms of autism spectrum disorder (ASD). Their relationship is reported, but existing data are conflicting as to whether they are related but distinct, o...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional r...

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets.

EBioMedicine
BACKGROUND: Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and t...

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies.

Computer methods and programs in biomedicine
BACKGROUND: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.

Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.

BMC gastroenterology
PURPOSE: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defecation. The condition is classified as a disorder of the gut-brain interaction (DGBI), and patients w...