Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

By Orestis Tsinalis, Paul M. Matthews, Yike Guo, Stefanos P. Zafeiriou. arXiv.org, 2016

331 citations recorded by the source corpus.

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.

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

View Paper: https://www.semanticscholar.org/paper/8dc2734c027ca11028b358b3f46d552466474c7b

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Citations in the corpus

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, neutral): “0 CNN [10], [17] − − − − − − − − − − 77.”
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, neutral): “[32] Sleep-EDF-13 20-fold CV Fpz-Cz 74.”
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (2018, neutral): “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].”
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017, methodology): “, convolution) to extract time-invariant features from raw FpzCz EEG channel [11].”
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, methodology): “mance on this dataset: (1) independent/dependent testing and (2) whether or not using only in-bed parts of the recordings as recommended in [16], [20], [21], [46].”
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, background): “Early attempts tried to use vanilla deep network architectures, such as deep neural networks (DNNs) [18], convolutional neural networks (CNNs) [17], [19]–[22], and recurrent neural networks (RNNs) [23].”
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017, methodology): “The highest misclassification rate for this stage is for assigning epochs to R stage (25%), followed by W (17%) and finally N2 (13%); a behaviour that is consistent with the literature [12, 13].”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “We recall that Tsinalis et al. 2016 network was originally fed with 150 s samples.”
Targeting EEG/LFP Synchrony with Neural Nets (2017, methodology): “This includes Restricted Boltzmann Machines and Deep Belief Networks [16, 35], CNNs [31, 28], and RNNs [2, 33].”
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021, methodology): “For example, the studies in [13], [17], [20] replicated the minority classes for model training.”