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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 761536, 8 pages
Research Article

A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction

1Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea
2Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Received 27 September 2013; Revised 6 January 2014; Accepted 10 January 2014; Published 26 February 2014

Academic Editor: Imre Cikajlo

Copyright © 2014 Ngoc Anh Nguyen Thi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.