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

By Orestis Tsinalis, Paul M. Matthews, Yike Guo. Annals of Biomedical Engineering, 2015

330 citations recorded by the source corpus.

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 skewed performance in favor of the most represented sleep stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75–80%), and high mean F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}-score (84%, range 82–86%) and mean accuracy across individual sleep stages (86%, range 84–88%) over all subjects. The performance of our method appears to be uncorrelated with the sleep efficiency and percentage of transitional epochs in each recording.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4928546bd044a28e1f4c06abb7edf5f6eefa7897

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Citations in the corpus

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, methodology): “To do so we also compared the ﬁlters and activation patterns per ﬁlter per sleep stage with the frequency content and activation patterns of the hand-engineered Morlet wavelets we used in [28].”
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (2018, unknown): “Several types of deep network architectures exist and have been proposed for automatic sleep scoring: Convolutional Neural Networks (CNNs) [8], [10], [11], [13]–[15], Deep Belief Networks (DBNs) [22], Auto-encoder [23], Deep Neural Networks (DNNs), and Recurrent Neural Networks (RNNs) [24].”
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, methodology): “Other network variants, such as Deep Belief Networks (DBNs) [28], Auto-encoder [21], Deep Neural Networks (DNNs) [23], have also been explored.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “An automatic sleep stage scoring model that uses a single channel of EEG was proposed in Tsinalis et al. (2016).”
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, methodology): “Due to availability of large sleep EEG recordings [20], deep learning algorithms have also been applied for sleep stage classiﬁcation [1], [21], [22], [23], [24] and other.”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “Indeed, some transitions are prohibited or others are strengthened depending on the occurence of some events such as arousal, K-complex or spindles regarding the transition N1-N2 [2], [4].”
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017, methodology): “As can be seen, both VGG-FE and VGG-FT exhibit state-of-the-art results, VGG-FT aligning with the best model to date [12].”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, unknown): “, 2018], [4] [Tsinalis et al., 2016], [5] [Andreotti et al.”
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, background): “Similar to previous works [17], [18], [19], [21], [22], [23], N3 and N4 stages were considered as N3 collectively and MOVEMENT and UNKNOWN categories were excluded.”
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017, methodology): “There have been a number of studies trying to develop a method to automate sleep stage scoring based on multiple signals such as EEG, EOG and EMG [4]–[6], or single-channel EEG [7]–[9].”
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation (2016, methodology): “org/physiobank/database/sleep-edfx/) and has been widely used in the literature [2,7,11,12,16,18,24,33,36,38,48,49,57,87,91,105,106,108,109,114].”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “Toical R eiew 24 [123] SAE Sigmoid (AE) f—WD 0.”