Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

By B. Kemp, Aeilko H. Zwinderman, B. Tuk, H. Kamphuisen, J. Oberye. IEEE Transactions on Biomedical Engineering, 2000

994 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/302838d005dc4a54f5ca05ff97c0dab831c4a6cf

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017, methodology): “We use EEG sleep recordings from the Sleep-EDF Database [26] in the PhysioNet repository [27].”
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, methodology): “SleepEDF-78: This database is the 2018 version of SleepEDF Expanded dataset [39], [40], consists of 78 healthy Caucasian subjects aged 25-101.”
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017, methodology): “We evaluated our model using different EEG channels from two public datasets: Montreal Archive of Sleep Studies (MASS) [26] and Sleep-EDF [27], [28].”
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, methodology): “The dataset that we used to evaluate our method is a publicly available sleep PSG dataset [14] from the PhysioNet repository [8] that can be downloaded from [21].”
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, background): “In fact, for both the full and linear model architectures trained with the SSC data, fine-tuning the pre-trained model is mostly on par with the randomly initialized counterpart.”
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, methodology): “SleepEDF-78: This database is the 2018 version of the Sleep-EDF Expanded dataset [42], [43], consisting of 78 healthy Caucasian subjects aged 25-101.”
ISRUC-Sleep: A comprehensive public dataset for sleep researchers (2016, neutral): “[40], Yaghouby et al.”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2017 [151] Sleep stage classification EOG, EEG DBN sleep EDF DB [Expanded] in Physionet [129, 152] Accuracy: 83.”
Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal (2014, methodology): “The experimental data used in this study were obtained from the Sleep-EDF database [25], [26], which was part of Physionet data bank [27].”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, methodology): “ividuals. We brieﬂy describe each dataset and refer to the original papers for details. Please refer to Table 1 for an overview and a list of used EEG channels. Sleep-EDF A public PhysioNet database [Kemp et al., 2000, Goldberger et al., 2000] often used for benchmarking automatic sleep stage classi”
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, methodology): “iv) Performance-wise, we demonstrate experimentally good performance on two publicly available datasets: SleepEDF [42], [43] with 20 subjects and MASS [36], a large sleep dataset with 200 subjects.”
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, methodology): “In this study, we used the Physionet Sleep-EDF dataset [30], [20]: version 1 contributed in 2013 with 61 polysomnograms (PSGs) and version 2 contributed in 2018 with 197 PSGs to evaluate the performance of the proposed method for the sleep stage scoring task.”
Chaos as an intermittently forced linear system (2016, neutral): “Data is available at [39, 48]: https://physionet.”
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders (2015, methodology): “The dataset that we used to evaluate our method is a publicly available sleep PSG dataset [14] from the PhysioNet repository [7] that can be downloaded from [18].”