5 November 1993 Wavelet transforms for electroencephalographic spike and seizure detection
Author Affiliations +
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven J. Schiff, John G. Milton, "Wavelet transforms for electroencephalographic spike and seizure detection", Proc. SPIE 2036, Chaos in Biology and Medicine, (5 November 1993); doi: 10.1117/12.162700; https://doi.org/10.1117/12.162700
PROCEEDINGS
7 PAGES


SHARE
Back to Top