Case study I: the detection of electroencephalogram spikes

Publisher Summary This chapter presents a case study of the application of neural network tools to solve a real-world problem: the identification of spikes in a multichannel electroencephalogram(EEG) signal. It discusses the reason for the problem being important to solve. The presence of EEC waveforms identified as spikes usually indicates some sort of abnormal brain function. The polarity and amplitude patterns of the spikes often provide information on the location and severity of the abnormality, possibly including information such as whether or not seizures are focal, focused in one small volume. This information is used by neurologists while deciding on the corrective measures. The EEC spike detection system is being developed for use in the four-bed epileptic monitoring unit at The Johns Hopkins Hospital. Various versions of the system should also be suitable for use at many facilities that continuously monitor EEC signals.