Wavelet-based feature extraction for EEG classification

Author

Dixon, Teresa L. ; Livezey, Glenn T.

Author_Institution

Dept. of Signal Process., Pennsylvania State Univ., University Park, PA, USA

Volume

3

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1003

Abstract

This paper uses the wavelet transform to obtain descriptive features of EEG data from a prenatal drug exposure study. The interest is in analyzing the data to determine temporal and spectral information that can be used as a predictor of central nervous system dysfunction. While the FFT can be useful, it does not incorporate temporal information. The wavelet transform is applied because it provides a multiresolution decomposition in time and frequency and is therefore well suited for the analysis of transient nonstationary signals such as these. Results show that useful features are obtained from the histograms of the relative time durations of specific frequencies obtained via the wavelet transform

Keywords

electroencephalography; feature extraction; medical signal processing; neurophysiology; spectral analysis; wavelet transforms; EEG classification; FFT; central nervous system dysfunction predictor; electrodiagnostics; histograms; multiresolution decomposition; prenatal drug exposure study; relative time durations; specific frequencies obtained; spectral information; temporal information; transient nonstationary signals; wavelet-based feature extraction; Central nervous system; Data analysis; Drugs; Electroencephalography; Feature extraction; Frequency; Information analysis; Transient analysis; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.652681

Filename

652681