Clustering epileptiform discharges with an adaptive subspace self-organizing feature map: a simulation study

Abstract

We present the results of a study where synthetically generated Epileptiform Discharges (EDs) superimposed on normal background EEG are clustered by means of Kohonen's Self-Organizing Feature Map (SOFM) using a set of basis vectors representing adaptive subspaces in place of the more usual weight vector at each node of the network. A training set of synthetic EDs is generated using a spherical head model assuming current dipole ED generators. The synthetic EDs are superimposed onto normal background EEG and a preliminary pre-processing stage is used to extract Candidate EDs (CEDs) conshsting of ED and non-ED events. The data is clustered using an adaptive subspace algorithm and the resulting map is calibrated using the labeled synthetic data set. Preliminary results show that the SOFM is well suited to clustering the pre-processed CEDs, where strong clusters of 'real' EDs are evident. The next step of this research is to further our investigations into the clustering of EDs using real data extracted from the interictal EEG.