AIMC Topic: Sleep Wake Disorders

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Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals.

Nature communications
Sleep disorders affect billions globally, yet diagnostic access remains limited by healthcare resource constraints. Here, we develop a deep learning framework that analyzes respiratory signals for remote sleep health monitoring, trained on 15,785 nig...

Assessing the association between multiple indicators of inflammation and sleep disorders in young and middle-aged women: insights from traditional and machine learning approaches.

European journal of medical research
BACKGROUND: Interactions between inflammation and sleep disorders are increasingly recognized; however, limited research comprehensively evaluates the association between multiple inflammatory indicators and sleep disorders.

Exploring nationwide patterns of sleep problems from late adolescence to adulthood using machine learning.

Science advances
Sleep problems among young adults pose a major public health challenge. Leveraging nationwide health surveys and registers from Denmark, we investigated patterns of sleep problems from late adolescence to adulthood and explored early life-course dete...

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...

The future of paediatric sleep medicine: a blueprint for advancing the field.

Journal of sleep research
Paediatric sleep medicine has rapidly evolved and expanded over the past half century as it became increasingly recognised as a unique field related to but distinct from adult sleep medicine. In looking forward to the next years, the focus of the fol...

About Digitalisation and AI, Data Protection, Data Exchange, Data Mining-Legal Constraints/Challenges Concerning Sleep Medicine.

Journal of sleep research
The revolution of artificial intelligence (AI) methods in the scope of the last years has inspired a deluge of use cases but has also caused uncertainty about the actual utility and boundaries of these methods. In this overview, we briefly introduce ...

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, ...