title: Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.
abstract: BACKGROUND
  Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring.
  
  
  METHODS
  Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation.
  
  
  RESULTS
  The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively.
  
  
  METHODS
  The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis.
  
  
  CONCLUSIONS
  The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection.
authors: Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
authors: Tarek
authors: T
authors: Lajnef
authors: Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
authors: Sahbi
authors: S
authors: Chaibi
authors: DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France.
authors: Perrine
authors: P
authors: Ruby
authors: DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France.
authors: Pierre-Emmanuel
authors: PE
authors: Aguera
authors: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
authors: Jean-Baptiste
authors: JB
authors: Eichenlaub
authors: Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
authors: Mounir
authors: M
authors: Samet
authors: Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia; Higher Institute of Industrial Systems of Gabes (ISSIG), University of Gabes, Gabes, Tunisia.
authors: Abdennaceur
authors: A
authors: Kachouri
authors: DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France; Psychology Department, University of Montreal, QC, Canada. Electronic address: karim.jerbi@umontreal.ca.
authors: Karim
authors: K
authors: Jerbi
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D003663:Decision Trees; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods; D004585:Electrooculography / Q000379:methods; D006801:Humans; D016014:Linear Models; D017286:Polysomnography / Q000379:methods*; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*; D060388:Support Vector Machine
keywords: Decision-tree; Dendrogram; Electroencephalography (EEG); Hierarchical clustering; Linear Discriminant Analysis (LDA); Machine learning; Oscillations; Polysomnography; Sleep scoring; Support vector machine (SVM)