title: Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.
abstract: BACKGROUND
  Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation.
  
  
  METHODS
  Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search.
  
  
  RESULTS
  The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively.
  
  
  METHODS
  The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR.
  
  
  CONCLUSIONS
  The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals.
authors.affiliation: School of Communication and Information Engineering, Shanghai University, China. Electronic address: junshi@staff.shu.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Shi
authors.affiliation: School of Communication and Information Engineering, Shanghai University, China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Liu
authors.affiliation: Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Communication and Information Engineering, Shanghai University, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: School of Communication and Information Engineering, Shanghai University, China.
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Mathematics, School of Science, Shanghai University, China. Electronic address: shying@shu.edu.cn.
authors.forename: Shihui
authors.initials: S
authors.lastname: Ying
mesh_terms: D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D017286:Polysomnography / Q000379:methods*; D012680:Sensitivity and Specificity; D012891:Sleep Apnea Syndromes / Q000145:classification / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology*
keywords: Extreme learning machine; Joint collaborative representation; Joint sparse representation; Multi-channel electroencephalography; Multiple kernel learning; Sleep staging