AIMC Topic: Sleep Stages

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A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram.

International journal of environmental research and public health May 23, 2022
The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not cl...
Electroencephalography Sleep Sleep Stages Neural Networks, Computer Deep Learning
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Sleep staging classification based on a new parallel fusion method of multiple sources signals.

Physiological measurement Apr 28, 2022
In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for sleep staging is to analyze Polysomnograms (PSGs) recorded in a sleep lab, but the sleep monitoring w...
Electrooculography Sleep Stages Electroencephalography Sleep Humans Algorithms Support Vector Machine
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Polysomnographic identification of anxiety and depression using deep learning.

Journal of psychiatric research Mar 24, 2022
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study re...
Deep Learning Depression Humans Anxiety Polysomnography Sleep Stages
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Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features.

Artificial intelligence in medicine Mar 9, 2022
This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a single-channel electroencephalogram (EEG). Bidirectional long short-term...
Electroencephalography Sleep Fourier Analysis Sleep Stages Neural Networks, Computer
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A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels.

IEEE journal of biomedical and health informatics Mar 7, 2022
In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres whe...
Humans Neural Networks, Computer Sleep Stages Electroencephalography Sleep Algorithms Infant, Newborn
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Convolutional neural network is a good technique for sleep staging based on HRV: A comparative analysis.

Neuroscience letters Feb 25, 2022
The fluctuation of heart rate is regulated by autonomic nervous system. In human sleep, the autonomic nervous system plays a leading role. Therefore, we can use heart-rate variability (HRV) to stage the sleep process. Based on two independent public ...
Sleep, REM Sleep Sleep Stages Neural Networks, Computer Heart Rate Humans
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Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.

Journal of neuroscience methods Nov 22, 2021
BACKGROUND: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjun...
Reproducibility of Results Animals Deep Learning Calcium Sleep Wakefulness Mice Sleep Stages Electroencephalography
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Inter-database validation of a deep learning approach for automatic sleep scoring.

PloS one Aug 16, 2021
STUDY OBJECTIVES: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restr...
Sleep Stages Algorithms Deep Learning Humans Databases, Factual Electroencephalography Sleep Neural Networks, Computer Polysomnography
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Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

IEEE journal of biomedical and health informatics Jul 27, 2021
Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional s...
Sleep Stages Polysomnography Sleep Deprivation Sleep Apnea, Obstructive Sleep Humans Deep Learning
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Cross-Gram matrices and their use in transfer learning: Application to automatic REM detection using heart rate.

Computer methods and programs in biomedicine Jul 21, 2021
BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years t...
Machine Learning Heart Rate Polysomnography Humans Sleep Stages
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