Automatic Classification of Sleep/Wake Stages Using Two-Step System

  • Lukáš Zoubek
  • Florian Chapotot
Part of the Communications in Computer and Information Science book series (CCIS, volume 188)

Abstract

This paper presents application of an automatic classification system on 53 animal polysomnographic recordings. A two-step automatic system is used to score the recordings into three traditional stages: wake, NREM sleep and REM sleep. In the first step of the analysis, monitored signals are analyzed using artifact identification strategy and artifact-free signals are selected. Then, 30sec epochs are classified according to relevant features extracted from available signals using artificial neural networks. The overall classification accuracy reached by the presented classification system exceeded 95%, when analyzed 53 polysomnographic recordings.

Keywords

decision making diagnosis medical applications pattern recognition signal processing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lukáš Zoubek
    • 1
  • Florian Chapotot
    • 2
  1. 1.Department of Information and Communication TechnologiesUniversity of OstravaOstravaCzech Republic
  2. 2.Department of MedicineThe University of ChicagoChicagoUSA

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