Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Journal: Journal of neuroscience methods
Published Date: Jan 25, 2015

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.

Authors

  • Tarek Lajnef
    Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
  • Sahbi Chaibi
    Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
  • Perrine Ruby
    DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France.
  • Pierre-Emmanuel Aguera
    DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France.
  • Jean-Baptiste Eichenlaub
    Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
  • Mounir Samet
    Sfax National Engineering School (ENIS), LETI Lab, University of Sfax, Sfax, Tunisia.
  • Abdennaceur Kachouri
    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.
  • Karim Jerbi
    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.

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