Wavelet entropy for subband segmentation of EEG during injury and recovery

Hasan A. Al-Nashash; Joseph S. Paul; Wendy C. Ziai; Daniel F. Hanley; Nitish V. Thakor

doi:10.1114/1.1575757

Wavelet entropy for subband segmentation of EEG during injury and recovery

Hasan A. Al-Nashash, Joseph S. Paul, Wendy C. Ziai, Daniel F. Hanley, Nitish V. Thakor

School of Medicine

Research output: Contribution to journal › Article › peer-review

39 Scopus citations

Abstract

A novel approach to segmentation of the fine changes in the subband electroencephalograph (EEG) measured during the period of recovery following a short instance of cerebral injury is presented. As such, distinct types of bursts had in general different types of frequency localizations and that the time-varying energies may be tracked by observing the temporal variations of the squares of the wavelet coefficients.

Original language	English (US)
Pages (from-to)	653-658
Number of pages	6
Journal	Annals of biomedical engineering
Volume	31
Issue number	6
DOIs	https://doi.org/10.1114/1.1575757
State	Published - 2003

Keywords

Burst patterns
Subband segmentation
Wavelet entropy

ASJC Scopus subject areas

Biomedical Engineering

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10.1114/1.1575757

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abstract = "A novel approach to segmentation of the fine changes in the subband electroencephalograph (EEG) measured during the period of recovery following a short instance of cerebral injury is presented. As such, distinct types of bursts had in general different types of frequency localizations and that the time-varying energies may be tracked by observing the temporal variations of the squares of the wavelet coefficients.",

keywords = "Burst patterns, Subband segmentation, Wavelet entropy",

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AU - Paul, Joseph S.

AU - Ziai, Wendy C.

AU - Hanley, Daniel F.

AU - Thakor, Nitish V.

PY - 2003

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N2 - A novel approach to segmentation of the fine changes in the subband electroencephalograph (EEG) measured during the period of recovery following a short instance of cerebral injury is presented. As such, distinct types of bursts had in general different types of frequency localizations and that the time-varying energies may be tracked by observing the temporal variations of the squares of the wavelet coefficients.

AB - A novel approach to segmentation of the fine changes in the subband electroencephalograph (EEG) measured during the period of recovery following a short instance of cerebral injury is presented. As such, distinct types of bursts had in general different types of frequency localizations and that the time-varying energies may be tracked by observing the temporal variations of the squares of the wavelet coefficients.

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KW - Subband segmentation

KW - Wavelet entropy

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Wavelet entropy for subband segmentation of EEG during injury and recovery

Abstract

Keywords

ASJC Scopus subject areas

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