EEG waveform analysis by means of dynamic time-warping.

The feasibility of using dynamic time-warping (DTW) to cluster EEG waveforms was studied. DTW compresses and extends the time axes of pairs of digitized waveforms to reduce the effects of minor differences in shape due to noise and normal, random shape fluctuations. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity index in a clustering procedure. Experiments with simulated data revealed that DTW based clustering could distinguish between waves only slightly different in frequency, amplitude, peak location, or initial phase. DTW clustering was also applied to sharp waves and spikes taken from actual EEG data and compared with an approach based on features extracted from the waveforms, and one based on computing the peak-aligned difference between waveforms. The results indicated that the DTW approach yielded more homogeneous clusters than the other two methods.