Epileptic seizure detection

In this study, radial basis function (RBF) neural networks are used to identify seizure or preseizure states. As input to the RBF networks the study used raw EEG data, coefficients from a Fourier transform, and wavelet decomposition of the raw data. An RBF network consists of an input layer, a single hidden layer, and an output node. The use of half-second windows of raw data as input demonstrates the ability of the RBF network to learn differences in the patterns of ictal and interictal EEG data without feature extraction. Wavelet decomposition of the narrow window of raw data improves performance while transformation of a wider window, up to about five seconds, improves it even further. The ability of wavelet decomposition to transform five seconds of raw data into a vector of manageable length without substantial loss of relevant information makes it an effective tool for preprocessing EEG data