AIMC Topic: Sleep Stages
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Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram.
PloS one
Jul 2, 2024
BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep u...
Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.
Sensors (Basel, Switzerland)
Jun 30, 2024
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness dete...
Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach.
Physiology & behavior
Jun 23, 2024
Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected ...
SleepFC: Feature Pyramid and Cross-Scale Context Learning for Sleep Staging.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 19, 2024
Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue:...
A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.
IEEE transactions on pattern analysis and machine intelligence
Jun 5, 2024
In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decis...
Machine learning-empowered sleep staging classification using multi-modality signals.
BMC medical informatics and decision making
May 6, 2024
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...
Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.
IEEE transactions on neural networks and learning systems
May 2, 2024
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...
Expert-level sleep staging using an electrocardiography-only feed-forward neural network.
Computers in biology and medicine
Apr 29, 2024
Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomno...
ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.
Scientific reports
Apr 29, 2024
Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall per...
Physics-Informed Transfer Learning to Enhance Sleep Staging.
IEEE transactions on bio-medical engineering
Apr 22, 2024
OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hospital polysomnography due to its lower cost and lower disruption to the daily lives of patients, especially in the case of long-term monitoring. Machi...
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