Automatic Detection of Sleep Stages Using the EEG

Abstract

We present a method for the detection of sleep stages using the EEG (electroencephalogram). The method consists of four steps: segmentation, parameter extraction, cluster analysis, and classification. The parameters we compared were the parameters of Hjorth, the harmonic parameters and the relative band energy. For cluster analysis we used a modified version of the means algorithm. It is shown that the investigated parameters are capable of extracting information from the EEG relevant for sleep stage scoring. Using the modified K-means algorithm it is possible to find similar segments and hence automate the detection of sleep stages. However, extra information e.g. the ECG (electrocardiogram) or the EOG (electrooculogram) is probably necessary for a clear discrimination between the different sleep stages.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410182

Entities

People

  • I. Lemahieu
  • J. De Koninck
  • P. Van Hese
  • R. Van De Walle
  • W. Philips

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Automatic
  • Bandwidth
  • Computer Vision
  • Detection
  • Electrodes
  • Electroencephalography
  • Energy Bands
  • Extraction
  • Eye Movements
  • Frequency
  • Frequency Bands
  • Health Services
  • Medical Personnel
  • Physicians
  • Vector Spaces

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Vision.