Creating symbolic representations of electroencephalographic signals: An investigation of alternate methodologies on intracranial data

The electroencephalogram (EEG) is widely used in the investigation of neurological disorders. Continuous long-term EEG data offers the opportunity to assess patient health over long periods of time, and to discover previously unknown physiological phenomena. However, the sheer volume of information generated by long-term EEG monitoring also poses a serious challenge for both analysis and visualization. Symbolization has been successful in addressing information overload in many disciplines. In this paper, we present different approaches to transform EEG signals into symbolic sequences. This discrete symbolic representation reduces the amount of EEG data by several orders of magnitude and makes the task of discovering and visualizing interesting activity more manageable. We describe alternate methodologies to symbolize EEG data from patients with epilepsy. When evaluated on long-term intracranial data from 10 patients, our symbolization produced results that were consistent with clinical labels of seizures (for 97% of the seizures and 68% of the seizure segments), and often produced finer-grained distinctions.