Reduced data similarity-based matching for time series patterns alignment

We propose a similarity-based matching technique for the purpose of quasi-periodic time series patterns alignment. The method is based on combination of two previously published works: a modified version of the Douglas–Peucker line simplification algorithm (DPSimp) for data reduction in time series, and SEA for pattern matching of quasi-periodic time series. The previously developed SEA method was shown to be more efficient than the very popular DTW technique. The aim of the obtained ASEAL method (Approximate Shape Exchange ALgorithm) is reduction of the space and time necessary to accomplish alignments comparable to those of the SEA method. The study shows the effectiveness of the proposed ASEAL method on ECG signals taken from the Massachusetts Institute of Technology – Beth Israel Hospital (MIT–BIH) database in terms of the correlation factor and alignment quality, for savings up to 90% in used samples and processing time reduction up to 97% with respect to those of SEA. Particularly, the method is able to deal with very complex alignment situations (magnitude/time axis shift/scaling, local variabilities, difference in length, phase shift, arbitrary number of periods) in the context of quasi-periodic time series. Among other possible applications, the proposed ASEAL method is a novel step toward resolution of the ‘person identification using ECG’ problem.