Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal

By Guohun Zhu, Yan Li, Peng (Paul) Wen. IEEE journal of biomedical and health informatics, 2014

351 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/587512a58e59d93229adc340de821c61244159e1

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

1. Yong Zou
2. Norbert Marwan
3. Jonathan F. Donges
4. Reik V. Donner
5. Jürgen Kurths
6. Cuntai Guan
7. Fabien Lotte
8. Dongrui Wu

Citations in the corpus

• An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021, methodology): “Second, the selected features are then fed into conventional machine learning models for sleep stage classification, such as Naive Bayes [8], support vector machines (SVM) [9], [10], random forest (RF) [7], [11], or even ensemble learning based classi-fiers [12].”
• Complex network approaches to nonlinear time series analysis (2019, background): “[237] introduded the concept of difference visibility graphs (DVGs), which are based on the node and edge sets of the natural VG with the edges of the HVG being removed, i.”