Results 41 to 50 of about 187,501 (315)

Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage [PDF]

arXiv, 2023
As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way.In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest.
arxiv  

Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study

Frontiers in Human Neuroscience, 2018
Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application.
Annette Sterr, James K. Ebajemito, Kaare B. Mikkelsen, Maria A. Bonmati-Carrion, Nayantara Santhi, Ciro della Monica, Lucinda Grainger, Giuseppe Atzori, Victoria Revell, Stefan Debener, Stefan Debener, Derk-Jan Dijk, Derk-Jan Dijk, Maarten DeVos   +13 more
doaj   +1 more source

Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder. [PDF]

Nat Sci Sleep
Qian Yang,1 Lingfeng Liu,1 Jing Wang,2 Ying Zhang,2 Nan Jiang,3 Meiyun Zhang2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121,
Yang Q, Liu L, Wang J, Zhang Y, Jiang N, Zhang M.   +5 more
europepmc   +2 more sources

A Review of Cerebral Hemodynamics During Sleep Using Near-Infrared Spectroscopy

Frontiers in Neurology, 2020
Investigating cerebral hemodynamic changes during regular sleep cycles and sleep disorders is fundamental to understanding the nature of physiological and pathological mechanisms in the regulation of cerebral oxygenation during sleep.
Haoran Ren, Xinyu Jiang, Ke Xu, Chen Chen, Yafei Yuan, Chenyun Dai, Wei Chen, Wei Chen, Wei Chen   +8 more
doaj   +1 more source

Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging [PDF]

arXiv, 2023
Sleep staging has become a critical task in diagnosing and treating sleep disorders to prevent sleep related diseases. With growing large scale sleep databases, significant progress has been made toward automatic sleep staging. However, previous studies face critical problems in sleep studies; the heterogeneity of subjects' physiological signals, the ...
arxiv  

Ssleepnet: a structured sleep network for sleep staging based on sleep apnea severity

Complex & Intelligent Systems, 2023
Sleep stage classification is essential in evaluating sleep quality. Sleep disorders disrupt the periodicity of sleep stages, especially the common obstructive sleep apnea (OSA).
Xingfeng Lv, Jun Ma, Jinbao Li, Qianqian Ren   +3 more
doaj   +1 more source

Classification of sleep stages from EEG, EOG and EMG signals by SSNet [PDF]

arXiv, 2023
Classification of sleep stages plays an essential role in diagnosing sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In this study, we propose an end-to-end deep learning architecture, named SSNet, which comprises of two deep learning networks based on Convolutional Neuron Networks (CNN) and Long Short Term Memory (LSTM).
arxiv  

Cellular and neurochemical basis of sleep stages in the thalamocortical network

eLife, 2016
The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic ...
Giri P Krishnan, Sylvain Chauvette, Isaac Shamie, Sara Soltani, Igor Timofeev, Sydney S Cash, Eric Halgren, Maxim Bazhenov   +7 more
doaj   +1 more source

Automated scoring of pre-REM sleep in mice with deep learning

Scientific Reports, 2021
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies ...
Niklas Grieger, Justus T. C. Schwabedal, Stefanie Wendel, Yvonne Ritze, Stephan Bialonski   +4 more
doaj   +1 more source

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance [PDF]

arXiv, 2023
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using Wavelets for feature extraction and Random Forest for classification, an automatic sleep-stage classification method was sought ...
arxiv