AIMC Topic: Sleep Stages
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A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.
International journal of neural systems
Dec 31, 2024
Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. Howe...
Detection and location of EEG events using deep learning visual inspection.
PloS one
Dec 23, 2024
The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject's health state in relation to ...
Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.
Computers in biology and medicine
Dec 10, 2024
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencep...
Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlabeled data. However, traditional SSL methods typically focus on single-view learning and do not fully...
ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
Dec 5, 2024
By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, exis...
CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signal...
Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.
Neurocase
Dec 4, 2024
Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain ac...
AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Nov 15, 2024
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional r...
Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.
Sleep medicine
Sep 29, 2024
OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including p...
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.
Sensors (Basel, Switzerland)
Sep 27, 2024
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this...
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