SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

By Sajad Mousavi, Fatemeh Afghah, Usha R. Acharya. PLoS ONE, 2019

477 citations recorded by the source corpus.

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) 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. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/40362f484c74343dd9f67da41d6ef1a1aea21301

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Citations in the corpus

SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, methodology): “Using this framework, handling a long context of 20-30 consecutive PSG epochs simultaneously, various advanced architectures have been proposed, for example CNN+RNN [24], [25], hierarchical RNN [15], [26], and CNN+Transformer [27].”
U-Sleep: resilient high-frequency sleep staging (2021, unknown): “While several high-performance deep-learning-based sleep staging systems have been proposed recently 28 – 38 , these have not yet been widely adopted in clinical practice because it is not clear if the reported results can be generalized.”
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, methodology): “Another notable difference with the SSC dataset was the scale of available labels, which seems to have enabled prior work to consider deeper and more complex models [47].”
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, unknown): “0 − − − − − − − − − − − − SleepEEGNet [52] 80.”
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021, methodology): “• SleepEEGNet [24] employs the same CNN architecture as DeepSleepNet [20] followed by an encoder-decoder with attention mechanism.”