Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.
By Tarek Lajnef, S. Chaibi, Perrine Ruby, P. Aguera, Jean-Baptiste Eichenlaub, Mounir Samet, A. Kachouri, K. Jerbi. Journal of Neuroscience Methods, 2015
311 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Physiology
View Paper: https://www.semanticscholar.org/paper/c6cf69976f1abc19e4ee4f138d8a49c0e5803b3d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Oliver Y. Chen
- Huy P Phan
- M. de Vos
- Fernando Andreotti
- Navin Cooray
- Alexandre Gramfort
- Gilles Wainrib
- Stanislas Chambon
Citations in the corpus
- A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “By doing so, every sleep stage has the same impact on the final figure of merit [16].”
- Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, background): “As a result, EOG and EMG are valuable additional sources, complementing EEG in multimodal automatic sleep staging systems [17], [18], [37], [38], [62], [63], not to mention their importance in manual scoring rules [33], [44].”
- SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (2018, unknown): “Apart from an EEG channel, an EOG and EMG channel were included to complement the EEG as they have been shown to be valuable addition sources for automatic sleep staging [8], [11]–[14], [42], [43].”
- An open-source, high-performance tool for automated sleep staging (2021, methodology): “In addition, several nonlinear features are calculated, including the number of zerocrossings, the Hjorth parameters of mobility and complexity (Hjorth, 1970), the permutation entropy (Bandt and Pompe, 2002; Lajnef et al., 2015), and the fractal dimension (Esteller et al.”
- DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017, background): “tiple 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): “Welch’s spectral analysis method and periodogram are examples of non-parametric approaches [41,46,129].”