Introduction to hierarchical clustering.

Hierarchical clustering of spike events is a method of grouping events that are similar in topology, morphology, or both, and it provides a method of efficient, detailed analysis of interictal events. Information about the relative populations of spikes at multiple foci is presented, and artifact events are grouped and eliminated en masse. The process of hierarchical clustering is explained, and a set of simulated traces is used to illustrate the process of hierarchical clustering and the development of a cluster tree to display the relative populations of similar spike events. Using EEG data from long-term monitoring, the use of a "review wizard" is explored as a means of structuring the process of hierarchical clustering and traversing the cluster tree. This aid is also used to streamline the process of determining the similarity of events within each group and of verifying that events exhibiting clinically important differences are not hidden within the groups comprising the average traces.