AIMC Topic: 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...
Humans Algorithms Support Vector Machine Signal Processing, Computer-Assisted Wavelet Analysis Sleep Stages Least-Squares Analysis Electroencephalography
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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...
Polysomnography Sleep Stages Humans Electroencephalography Sleep 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...
Neural Networks, Computer Databases, Factual Brain Humans Sleep Stages Wakefulness Normal Distribution Brain Mapping Electroencephalography
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Uncovering the structure of clinical EEG signals with self-supervised learning.

Journal of neural engineering Mar 31, 2021
Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of s...
Electroencephalography Supervised Machine Learning Neural Networks, Computer Research Design Sleep Stages Humans
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Hybrid manifold-deep convolutional neural network for sleep staging.

Methods (San Diego, Calif.) Feb 24, 2021
Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective fa...
Neural Networks, Computer Electroencephalography Sleep Stages Sleep Humans
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Deep Learning for Automated Feature Discovery and Classification of Sleep Stages.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2020
Convolutional neural networks (CNN) have demonstrated state-of-the-art classification results in image categorization, but have received comparatively little attention for classification of one-dimensional physiological signals. We design a deep CNN ...
Sleep Stages Humans Deep Learning Electroencephalography Computational Biology Signal Processing, Computer-Assisted
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Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor.

IEEE journal of biomedical and health informatics Dec 4, 2020
Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monit...
Neural Networks, Computer Machine Learning Polyvinyls Female Polysomnography Young Adult Humans Retrospective Studies Sleep Stages Adult Middle Aged Adolescent Male Signal Processing, Computer-Assisted
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Automatic sleep scoring: A deep learning architecture for multi-modality time series.

Journal of neuroscience methods Nov 4, 2020
BACKGROUND: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning archite...
Electroencephalography Polysomnography Sleep Sleep Stages Humans Deep Learning
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Sleep stage classification for child patients using DeConvolutional Neural Network.

Artificial intelligence in medicine Nov 2, 2020
Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characte...
Aged Child Sleep Humans Polysomnography Child, Preschool Electroencephalography Neural Networks, Computer Sleep Stages
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