Analysis of brain function and classification of sleep stage EEG using daubechies wavelet

Min Soo Kim, Young Chang Cho, Abibullaev Berdakh, Hee Don Seo

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Recently, wavelet transforms have been applied to various problems in many fields. In this paper, we propose the application of the Daubechies wavelet to the detection of several important characteristic waves in electroencephalograms (EEGs), which are used to diagnose sleep stages and cognitive mental tasks. Sleep staging is one of the most important tasks in EEG diagnosis. However, it can be subjective as it depends on the doctor's skill and is omit labor-intensive. In this regard, the development of an automatic diagnosis system is imperative in order to reduce the doctor's workload and to provide an accurate quantitative diagnosis of sleep stage EEGs. The method proposed in this paper is an important base for understanding subjects' cognitive state. Experimental results obtained using the implemented system demonstrate that this approach could reduce the doctor's workload and provide an accurate diagnosis of brain functions.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalSensors and Materials
Volume20
Issue number1
Publication statusPublished - Apr 1 2008

Keywords

  • Brain computer interface (BCI)
  • Cognitive mental state
  • Daubechies wavelet
  • Mental tasks
  • Sleep electroencephalograms
  • Sleep stages

ASJC Scopus subject areas

  • Instrumentation
  • Materials Science(all)

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