AIMC Topic: Sleep Stages

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SlumberNet: deep learning classification of sleep stages using residual neural networks.

Scientific reports Feb 27, 2024
Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture...
Polysomnography Sleep Stages Animals Deep Learning Electroencephalography Sleep Mice Neural Networks, Computer
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U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.

Computers in biology and medicine Feb 23, 2024
With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high an...
Humans Reproducibility of Results Aged Sleep Uncertainty Sleep Stages Deep Learning Sleep Apnea Syndromes
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Timing matters for accurate identification of the epileptogenic zone.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Feb 18, 2024
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intra...
Brain Female Young Adult Humans Machine Learning Middle Aged Adolescent Time Factors Adult Male Sleep Stages Electrocorticography Electroencephalography Epilepsy
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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...
Sleep Algorithms Humans Sleep Stages Deep Learning Artificial Intelligence Electroencephalography
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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...
Deep Learning Electroencephalography Calibration Sleep Stages Sleep Humans
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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...
Polysomnography Sleep Stages Electroencephalography Sleep Brain Humans Deep Learning
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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...
Algorithms Humans Sleep Deep Learning Privacy Sleep Stages
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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...
Polysomnography Sleep Stages Sleep Humans Movement Aged Adult Middle Aged Aged, 80 and over Deep Learning Young Adult
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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 Deep Learning Humans Sleep Apnea, Obstructive Sleep Radar
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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 Humans Sleep Stages Electroencephalography Algorithms
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