AIMC Topic: Sleep Stages
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Automated sleep staging model for older adults based on CWT and deep learning.
Scientific reports
Jul 1, 2025
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
Jun 6, 2025
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...
Time-series visual representations for sleep stages classification.
PloS one
May 21, 2025
Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. Smartwatches offer a practical, non-invasive, and cost-effective alternative ...
MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
May 6, 2025
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global tempora...
FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.
IEEE journal of biomedical and health informatics
May 6, 2025
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightw...
Data-driven sleep structure deciphering based on cardiorespiratory signals.
Computer methods and programs in biomedicine
Apr 23, 2025
BACKGROUND AND OBJECTIVE: Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) a...
Towards interpretable sleep stage classification with a multi-stream fusion network.
BMC medical informatics and decision making
Apr 14, 2025
Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods igno...
Detecting arousals and sleep from respiratory inductance plethysmography.
Sleep & breathing = Schlaf & Atmung
Apr 11, 2025
PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monit...
Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.
Scientific reports
Mar 29, 2025
Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and ...
Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.
Scientific reports
Mar 14, 2025
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life. The identification o...
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