AIMC Topic: Sleep Stages

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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...
Humans Sleep Calibration Sleep Stages Deep Learning Electroencephalography
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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 Stages Humans Brain Sleep Deep Learning Polysomnography Electroencephalography
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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...
Humans Algorithms Sleep Sleep Stages Deep Learning Privacy
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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...
Sleep Movement Middle Aged Deep Learning Polysomnography Sleep Stages Aged Aged, 80 and over Young Adult Adult Humans
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Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Journal of sleep research Sep 26, 2023
Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In ...
Sleep Stages Sleep Radar Humans Deep Learning Sleep Apnea, Obstructive
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3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 7, 2023
A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EE...
Electromyography Deep Learning Sleep Stages Electroencephalography Humans Algorithms
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Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.

Sensors (Basel, Switzerland) Jul 17, 2023
Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has ...
Computers Sleep Stages Wakefulness Blinking Deep Learning Automobile Driving
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Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.

Sleep health Jun 26, 2023
GOAL AND AIMS: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.
Sleep Sleep Stages Wearable Electronic Devices Humans Sleep Apnea, Obstructive Deep Learning
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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...
Sleep Wake Disorders Sleep Stages Sleep Humans Polysomnography Artificial Intelligence
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TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging.

IEEE transactions on cybernetics Jun 15, 2023
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those meth...
Electroencephalography Sleep Sleep Stages Neural Networks, Computer Machine Learning
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