AIMC Topic: Sleep Stages

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A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique.

Neuroscience research Jul 17, 2021
Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose...
Algorithms Animals Electroencephalography Sleep Deep Learning Sleep Stages Mice
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Automated scoring of pre-REM sleep in mice with deep learning.

Scientific reports Jun 10, 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 accurac...
Sleep, REM Polysomnography Animals Sleep Stages Deep Learning Models, Theoretical Mice Algorithms Sleep
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Real-time, automatic, open-source sleep stage classification system using single EEG for mice.

Scientific reports May 27, 2021
We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitg...
Mice Electroencephalography Electromyography Algorithms Electrodes Signal Processing, Computer-Assisted Sleep Stages Animals Neural Networks, Computer Superficial Back Muscles
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Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.

Scientific reports May 25, 2021
Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of ...
Polysomnography Sleep Stages Cattle ROC Curve Models, Biological Heart Rate Myocardium Neural Networks, Computer Machine Learning Animals
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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

IEEE transactions on bio-medical engineering May 21, 2021
BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues....
Humans Neural Networks, Computer Machine Learning Sleep Stages Sleep Polysomnography
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A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation.

Journal of neural engineering May 18, 2021
Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psychophysiological states. Recently, numerous works applied deep learning techniques, especially the conv...
Neural Networks, Computer Algorithms Electroencephalography Humans Sleep Sleep Stages
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Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Neuroscience research May 11, 2021
Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy bec...
Sleep Stages Support Vector Machine Least-Squares Analysis Signal Processing, Computer-Assisted Wavelet Analysis Electroencephalography Humans Algorithms
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A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sensors (Basel, Switzerland) May 11, 2021
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requiremen...
Electroencephalography Sleep Polysomnography Humans Sleep Stages Deep Learning
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An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society May 5, 2021
Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. Th...
Sleep Stages Electroencephalography Sleep Neural Networks, Computer Deep Learning
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Neurophysiological brain mapping of human sleep-wake states.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Apr 1, 2021
OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to inclu...
Wakefulness Humans Normal Distribution Sleep Stages Neural Networks, Computer Databases, Factual Brain Mapping Brain Electroencephalography
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