CaseNet: a neural network tool for EEG waveform classification

The development of a system to detect online multichannel epileptiform spikes is described. Three main topics are discussed. The first is the preprocessing procedure used on the raw data prior to their presentation to the neural network. Issues reviewed include tradeoffs between preprocessing and system complexity. The second is the development of CaseNet, a neural network development tool used to graphically specify a network architecture from which executable code is generated automatically. Areas discussed include selection of the network architecture, such as choices between supervised and unsupervised learning schemes. The third concerns the interim results of the analysis of single- and four-channel electroencephalogram (EEG) data. The relationship of the spike detection effort to a similar one for seizure detection is also outlined