Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

@article{Tsinalis2015AutomaticSS,
  title={Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders},
  author={Orestis Tsinalis and Paul M. Matthews and Yike Guo},
  journal={Annals of Biomedical Engineering},
  year={2015},
  volume={44},
  pages={1587 - 1597}
}
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the sleep stages. We used class-balanced random sampling across sleep stages for each model in the ensemble to avoid… 
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
TLDR
It is shown that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages, and these results are comparable to state-of-the-art methods with hand-engineered features.
A convolutional neural network for sleep stage scoring from raw single-channel EEG
TLDR
A deep convolutional neural network is introduced on raw EEG samples for supervised learning of 5-class sleep stage prediction and a method for visualizing class-wise patterns learned by the network is presented.
An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model
TLDR
The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.
Automatic Classification of Sleep Stages Based on Raw Single-Channel EEG
TLDR
A model for automatic sleep stage classification based on raw single-channel EEG that can preserve the information, broaden the network and enlarge the receptive field as much as possible to extract appropriate time invariant features and classify sleep stage well is designed.
Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal
TLDR
The proposed sleep staging system based on an ensemble learning stacking model that integrates Random Forest and eXtreme Gradient Boosting has an excellent improvement in classification accuracy for the six-two sleep states classification.
A Two-Phase Model for Sleep Staging Using Single Channel EEG
TLDR
A two phase model, based on deep neural networks and support vector machine for automatic sleep staging using raw single channel EEG signals that can learn richer features from raw EEG signals automatically, and achieves a better accuracy than existing methods.
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach
TLDR
An automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores.
Time-Frequency Convolutional Neural Network for Automatic Sleep Stage Classification Based on Single-Channel EEG
  • Liangjie Wei, Youfang Lin, Jing Wang, Yan Ma
  • Computer Science
    2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)
  • 2017
TLDR
The results show that the proposed method give good classifications for most sleep stages, especially for awake and the SWS stages, which outperforms other four existing methods.
Automatic Sleep Stage Classification using Convolutional Neural Networks with Long Short-Term Memory
TLDR
A convolutional neural network is trained to automatically extract features from raw 30-second epochs of the EEG, EMG and EOG and learns features similar to the ones described in the sleep scoring manual of the American Academy of Sleep Medicine.
Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification
  • Tianqi Zhu, Wei Luo, Feng Yu
  • Computer Science, Medicine
    International journal of environmental research and public health
  • 2020
TLDR
A neural network based on a convolutional network and attention mechanism to perform automatic sleep staging and the attention mechanism excels in learning inter- and intra-epoch features is proposed.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 31 REFERENCES
Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models
TLDR
An automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms is proposed.
Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier
TLDR
An efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy.
Automatic analysis of single-channel sleep EEG: validation in healthy individuals.
TLDR
The results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals and appears as a good candidate for diagnostic aid and automatic ambulant scoring.
Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters.
TLDR
It is demonstrated that automated scoring of sleep obtained from a single-channel of forehead EEG results in agreement to majority manual scoring are similar to results obtained from studies of manual interrater agreement.
Interobserver agreement among sleep scorers from different centers in a large dataset.
TLDR
The level of agreement in sleep stage assignment varies between scorers, by diagnosis, and by record, and this warrants caution when comparing data scored in separate laboratories.
The visual scoring of sleep in adults.
  • M. Silber, S. Ancoli-Israel, +9 authors C. Iber
  • Psychology, Medicine
    Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
  • 2007
TLDR
The work of the AASM Visual Scoring Task Force is described, including methodology, a literature review and the rationale behind the new rules, which define onset and termination of REM sleep periods and propose alternative measures for non-alpha generating subjects.
Functional topography of the human nonREM sleep electroencephalogram
TLDR
Results indicate that changes in sleep propensity are reflected by specific regional differences in EEG power, and the predominant increase of low‐frequency power in frontal areas may be due to a high ‘recovery need’ of the frontal heteromodal association areas of the cortex.
Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG
TLDR
Slow-wave microcontinuity, being based on a physiological model of sleep, reflects sleep depth more closely than SWP does, and confirms earlier reports that gender affects SWP but not sleep depth.
Excessive daytime sleepiness and sudden-onset sleep in Parkinson disease: a survey by the Canadian Movement Disorders Group.
TLDR
Excessive daytime sleepiness is common even in patients with PD who are independent and do not have dementia, and its specificity can be increased by use of the Inappropriate Sleep Composite Score.
Sleep and depression — results from psychobiological studies: an overview
TLDR
Data indicate a strong bi-directional relationship between sleep, sleep alterations and depression, and most of the effective antidepressant agents suppress REM sleep.
...
1
2
3
4
...