AIMC Topic: Sleep
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Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.
Sleep medicine reviews
Jan 11, 2024
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence tech...
Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.
Sleep medicine
Jan 11, 2024
BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal resp...
Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor.
Journal of sleep research
Jan 7, 2024
Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model f...
Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning Models.
IEEE journal of biomedical and health informatics
Jan 4, 2024
Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle bo...
A robust deep learning detector for sleep spindles and K-complexes: towards population norms.
Scientific reports
Jan 2, 2024
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variabili...
Sleep, physical activity and panic attacks: A two-year prospective cohort study using smartwatches, deep learning and an explainable artificial intelligence model.
Sleep medicine
Dec 22, 2023
BACKGROUND: Sleep and physical activity suggestions for panic disorder (PD) are critical but less surveyed. This two-year prospective cohort study aims to predict panic attacks (PA), state anxiety (SA), trait anxiety (TA) and panic disorder severity ...
Performance of artificial intelligence chatbots in sleep medicine certification board exams: ChatGPT versus Google Bard.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Dec 20, 2023
PURPOSE: To conduct a comparative performance evaluation of GPT-3.5, GPT-4 and Google Bard in self-assessment questions at the level of the American Sleep Medicine Certification Board Exam.
Sleep Apnea Prediction Using Deep Learning.
IEEE journal of biomedical and health informatics
Nov 7, 2023
Obstructive sleep apnea (OSA) is a sleep disorder that causes partial or complete cessation of breathing during an individual's sleep. Various methods have been proposed to automatically detect OSA events, but little work has focused on predicting su...
Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
Nov 7, 2023
Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported in past literature, uptake of automatic sleep scoring in the clini...
Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.
Scientific reports
Oct 18, 2023
Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespira...
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