A characterization of sleep spindles in EEG

Beena Ahmed, Amira Redissi, Reza Tafreshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The absence of an accurate sleep spindle detector has hindered sleep researchers in understanding its role in sleep and its effect on the human body. In this paper, sleep spindles, marked by a neurologist, have been analyzed to develop a better understanding of its characteristic features and enable the development of a robust and reliable sleep spindle detector. As sleep spindles are characterized by high periodicity features useful in differentiating between periodic and non-periodic signals were investigated. The highest discrimination was found to be provided by the zero-crossing rate, mean peak to valley distance, autocorrelation coefficient and wavelet packet energy ratio.

Original languageEnglish
Title of host publicationWorld Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering
Pages671-674
Number of pages4
Volume25
Edition7
DOIs
Publication statusPublished - 2009
EventWorld Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering - Munich, Germany
Duration: 7 Sep 200912 Sep 2009

Other

OtherWorld Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering
CountryGermany
CityMunich
Period7/9/0912/9/09

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Keywords

  • EEG
  • Sleep analysis
  • Sleep spindles
  • Wavelet packet tree
  • Wavelets

ASJC Scopus subject areas

  • Biomedical Engineering
  • Bioengineering

Cite this

Ahmed, B., Redissi, A., & Tafreshi, R. (2009). A characterization of sleep spindles in EEG. In World Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering (7 ed., Vol. 25, pp. 671-674) https://doi.org/10.1007/978-3-642-03885-3-186