Effectiveness of wavelet denoising on electroencephalogram signals

Md. Mamun Ibne Reaz, Mahmoud Al-Kadi, Mohd Marufuzzaman

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram, eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filtering suffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employed discrete wavelet transform. Root mean square difference has been used to find the usefulness of the noise elimination. In this research, four different discrete wavelet functions have been used to remove noise from the Electroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show the effectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one for noise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noise elimination from the EEG signal on healthy subjects.

Original languageEnglish
Pages (from-to)156-160
Number of pages5
JournalJournal of Applied Research and Technology
Volume11
Issue number1
Publication statusPublished - Feb 2013

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Electroencephalography
Discrete wavelet transforms

Keywords

  • Denoising
  • Discrete wavelet transform
  • Electroencephalogram
  • Root mean square

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Effectiveness of wavelet denoising on electroencephalogram signals. / Ibne Reaz, Md. Mamun; Al-Kadi, Mahmoud; Marufuzzaman, Mohd.

In: Journal of Applied Research and Technology, Vol. 11, No. 1, 02.2013, p. 156-160.

Research output: Contribution to journalArticle

Ibne Reaz, Md. Mamun ; Al-Kadi, Mahmoud ; Marufuzzaman, Mohd. / Effectiveness of wavelet denoising on electroencephalogram signals. In: Journal of Applied Research and Technology. 2013 ; Vol. 11, No. 1. pp. 156-160.
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