Mapping of chaotic patterns for localization of EEG abnormalities

While the usefulness of the electrocardiogram (ECG) has been demonstrated for diagnosis in cardiology, the same is not true for the diagnostic value of electroencephalograms (EEG) in neurological diagnosis. Whether the limited usefulness is due to the inherent lack of specificity of the EEG signal or the need for new methods of analysis remains an open question. In previous work, the authors have developed chaotic techniques that provide both graphical and numerical summaries of ECGs that have been demonstrated to give useful diagnostic results. The current work focuses on the adaptation of these techniques to the EEG, taking into account the major differences in the nature of the signal, the multi-channel data, and the potential interactions of the channels. The goal is to develop a computer model to provide graphical and numerical representations that can be used in a higher-order decision model based on the intelligent agent approach.