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Automated Sleep EEg Analysis using an RBF Network

  • Stephen Roberts
  • Lionel Tarassenko
Chapter

Abstract

There are many examples of expert systems which have been developed in the last twenty years in an attempt to solve medical diagnostic problems automatically (see, for example [1]). There are, however, a number of medical problems which do not lend themselves very well to the expert system’s approach. In this chapter, we focus on one such problem, namely the analysis of the electroencephalogram (EEG) during sleep. At present, a set of rules proposed more than twenty years ago [15] is still being used by human experts to classify successive 30-second segments of the EEG sleep record into one of six major categories (wake, dreaming sleep and four stages of progressively deeper sleep) but the rules are notoriously difficult to apply and inter-observer correlation can be as low as 51% for some sections of data [8]. The lack of agreement amongst trained human experts on all but very typical data segments has made the automation of the “sleep scoring” process an almost impossible task.

Keywords

Input Space Hide Unit Linear Classifier Disturbed Sleep Deep Sleep 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Stephen Roberts
    • 1
  • Lionel Tarassenko
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordUK

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