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Proceedings Paper

Wavelet transforms for electroencephalographic spike and seizure detection
Author(s): Steven J. Schiff; John G. Milton
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Paper Abstract

The application of wavelet transforms (WT) to experimental data from the nervous system has been hindered by the lack of a straightforward method to handle noise. A noise reduction technique, developed recently for use in wavelet cluster analysis in cosmology and astronomy, is here adapted for electroencephalographic (EEG) time-series data. Noise is filtered using control surrogate data sets generated from randomized aspects of the original time-series. In this study, WT were applied to EEG data from human patients undergoing brain mapping with implanted subdural electrodes for the localization of epileptic seizure foci. EEG data in 1D were analyzed from individual electrodes, and 2D data from electrode grids. These techniques are a powerful means to identify epileptic spikes in such data, and offer a method to identity the onset and spatial extent of epileptic seizure foci. The method is readily applied to the detection of structure in stationary and non-stationary time-series from a variety of physical systems.

Paper Details

Date Published: 5 November 1993
PDF: 7 pages
Proc. SPIE 2036, Chaos in Biology and Medicine, (5 November 1993); doi: 10.1117/12.162700
Show Author Affiliations
Steven J. Schiff, Children's National Medical Ctr. (United States)
John G. Milton, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 2036:
Chaos in Biology and Medicine
William L. Ditto, Editor(s)

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