Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Matching and indexing sequences of different lengths
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
The String-to-String Correction Problem
Journal of the ACM (JACM)
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Matching of quasi-periodic time series patterns by exchange of block-sorting signatures
Pattern Recognition Letters
Piecewise linear correction of ECG baseline wander: a curve simplification approach
Computer Methods and Programs in Biomedicine
Pattern Recognition
A model for popularity dynamics to predict hot articles in discussion blog
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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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.