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S. J. Schiff and J. Milton. 1993. Wavelet transforms for electroencephalographic spike and seizure detection. SPIE Chaos in Biology and Medicine 2036: 50-56. Full Article

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 implanated subdural electrodes for the localization of epileptic seizure foci. EEG data in 1-dimension (1D) were analyzed from individual electrodes, and 2-dimensional (2D) data from electrode grids. These techniques are a powerful means to identify epileptic spiks in such data, and offer a method to identify 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.

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