AIMC Topic: Sleep Wake Disorders
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A Deep Learning-Derived Transdiagnostic Signature Indexing Hypoarousal and Impulse Control: Implications for Treatment Prediction in Psychiatric Disorders.
Biological psychiatry. Cognitive neuroscience and neuroimaging
Aug 13, 2024
BACKGROUND: Psychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. The Research Domain Criteria (RDoC) constitute a research classification system for psychiatric disorders based on dimensi...
Automated remote sleep monitoring needs uncertainty quantification.
Journal of sleep research
Aug 7, 2024
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...
State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.
Scientific reports
Jul 14, 2024
Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was traine...
Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery.
Otolaryngologic clinics of North America
May 7, 2024
Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data for diagnosis and treatment of voice, swallowing, and sleep disorders. Clinicians face challenges in ...
Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.
Respiratory medicine
May 6, 2024
BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes.
Challenges of Applying Automated Polysomnography Scoring at Scale.
Sleep medicine clinics
Jun 15, 2023
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this tec...
Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning.
Sensors (Basel, Switzerland)
Mar 26, 2023
Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simulta...
Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.
International journal of environmental research and public health
Oct 18, 2022
Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the ana...
A review of automated sleep disorder detection.
Computers in biology and medicine
Sep 16, 2022
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monit...
Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.
Sleep & breathing = Schlaf & Atmung
Mar 9, 2022
BACKGROUND: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human ...
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