AIMC Topic: Sleep Stages

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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...
Humans Electroencephalography Supervised Machine Learning Sleep Stages Neural Networks, Computer Research Design
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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...
Sleep Sleep Stages Neural Networks, Computer Electroencephalography 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 ...
Electroencephalography Signal Processing, Computer-Assisted Sleep Stages Deep Learning Computational Biology Humans
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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...
Machine Learning Retrospective Studies Female Humans Neural Networks, Computer Signal Processing, Computer-Assisted Polyvinyls Sleep Stages Male Polysomnography Young Adult Middle Aged Adult Adolescent
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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 Sleep Sleep Stages Polysomnography 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...
Sleep Stages Sleep Polysomnography Neural Networks, Computer Humans Aged Child, Preschool Child Electroencephalography
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A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network.

European neurology Oct 29, 2020
INTRODUCTION: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of slee...
Wavelet Analysis Music Reproducibility of Results Databases, Factual Sleep Stages Adolescent Humans Male Young Adult Female Electroencephalography Neural Networks, Computer Adult Middle Aged
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Neonatal EEG sleep stage classification based on deep learning and HMM.

Journal of neural engineering Jun 25, 2020
OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve th...
Humans Infant Deep Learning Infant, Newborn Sleep Sleep Stages Electroencephalography Signal Processing, Computer-Assisted
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Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

Computers in biology and medicine Jun 7, 2020
BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
Sleep Stages Deep Learning Humans Electrocardiography Neural Networks, Computer Biometric Identification
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Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring.

Computers in biology and medicine Mar 7, 2020
In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred to as the "database variabil...
Databases, Factual Sleep Sleep Stages Neural Networks, Computer Machine Learning
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