Spike Recognition and On-Line Classification by Unsupervised Learning System

An on-line spike recognition system allows separation of multiple spikes present on a single channel, in up to six different classes. The learning phase is unsupervised, and uses the data samples of the waveform as coordinates in a multidimensional feature space. Additional signal characteristics may improve the system performance in special cases. Using the well known nearest neighbor technique, all possible cluster configurations are determined. From this analysis, the investigator selects the physiologically best suited duster layout, primary based on a curve showing the number of clusters versus the maximum distance of two neighboring spikes in the same cluster. This procedure is supported by visual examination of the spikes of each cluster. Statistics are calculated for inter-and intracluster distances, yielding confidence limits for the cluster bounds, and estimates for the quality of separation. During the classification phase, a separate graphic display processor permits continuous control without delay. Each classified spike is projected over its cluster, identifying mean waveform.