Spectral subtraction and cepstral distance for enhancing EEG entropy

Khaled Assaleh, Kasan Al-Nashash, Nitish Thakor

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

Abstract

Electroencephalographic (EEG) signals are normally acquired in the presence of background noise which causes inaccurate or false entropy measurement throughout the signal. In this paper, spectral subtraction is used to pre-process EEG signals to improve the accuracy of computing the subband wavelet entropy (SWE). The silent period in the EEG signal is identified via cepstral distance which allows its entropy to be set to zero. The EEG signal presented in this paper represents various stages of brain recovery obtained from a rodent following global cerebral ischemia. The various subband entropies are calculated using wavelet decomposition in EEG subbands, namely Delta, Theta, Alpha, Beta and Gamma. The utilization of spectral subtraction improved the accuracy of the SWE as compared to energy thresholding.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2751-2754
Number of pages4
ISBN (Print)0780387406, 9780780387409
DOIs
StatePublished - Jan 1 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
CountryChina
CityShanghai
Period9/1/059/4/05

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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