title: Automated morphological analysis by means of dynamic time-warping.
abstract: A new technique for the clustering of EEG wave forms is proposed. This method, termed dynamic time-warping (DTW) based clustering, involves the determination of a distance measure by allowing a certain degree of flexibility in the time axes of the two waves to be compared. Sharp waves and spikes, taken from actual EEG data, were subjected to the DTW-clustering approach. The results were compared with an approach based on features extracted from the wave forms and one based on computing the peak-aligned difference between wave forms. It was found that the DTW approach resulted in more homogeneous clusters than the other two approaches. These results, although preliminary, clearly indicate the feasibility of applying this new method for wave form clustering.
authors.forename: B H
authors.initials: BH
authors.lastname: Jansen
authors.forename: H C
authors.initials: HC
authors.lastname: Huang
mesh_terms: D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology