A convolutional neural network for sleep stage scoring from raw single-channel EEG

By Arnaud Sors, Stéphane Bonnet, S. Mirek, L. Vercueil, J. Payen. Biomedical Signal Processing and Control, 2018

397 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology

View Paper: https://www.semanticscholar.org/paper/e5845afacc4d38ee075bbb219d4e440346d9edd8

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Citations in the corpus

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021, methodology): “In [14], the authors used 12 convolution layers together with 2 fully connected layers.”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “On the contrary, [11], [13] extended the feature extractor input window to 150 s, respectively 120 s.”
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, neutral): “…Networks (DBNs) [16], Auto-encoders [23], Deep Neural Networks (DNNs) [24], Convolutional Neural Networks (CNNs) [17], [18], [20], [21], [25], [26], and Recurrent Neural Networks (RNNs) [19] are being surpassed by more complex, task-speciﬁc architectures such as DNN+RNN [24], CNN+RNN [22],…”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “A single-channel EEG-based method using CNN for 5-class sleep stage conjecture in [102] shows competitive performance in sensible pattern detection and visualization.”
Data Augmentation for Deep-Learning-Based Electroencephalography. (2020, methodology): “(2018) used a CNN on raw single-channel EEG signal for scoring 5 class sleep stage [45].”
Deep learning-based electroencephalography analysis: a systematic review (2019, methodology): “Several studies used backpropagation-based techniques to generate input maps that maximize activations of specific units [188, 144, 160, 15].”
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, methodology): “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].”