Neural network design considerations for EEG spike detection

Neural networks are being used to analyze electroencephalogram (EEG) signals for the detection of epileptiform spikes. A review is presented of the design considerations involved in implementing a real-time spike detection system. Issues addressed are generally in two areas. The first is the characterization of the source data. For example, decisions must be made relative to data rates, the number of data channels and whether to use raw data, or preprocessed data in the form of spike parameters. The second is the selection of the neural network architecture and the specific implementation of that architecture. For example, choices must be made between supervised and unsupervised learning schemes, and among the many available network learning algorithms. A discussion is presented of interim results in an EEG spike detection project, the goal of which is to provide real-time spike detection capability for a multibed epilepsy monitoring unit