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Complexity
Volume 2017, Article ID 1580414, 9 pages
https://doi.org/10.1155/2017/1580414
Research Article

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

1School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
2ICT College of Vocational Studies, Zdravka Čelara 16, 11000 Belgrade, Serbia
3Health Center “Zvezdara”, Olge Jovanovic 11, 11000 Belgrade, Serbia

Correspondence should be addressed to Ana Gavrovska; moc.liamg@777agana

Received 10 June 2017; Accepted 23 October 2017; Published 13 November 2017

Academic Editor: Roberto Tonelli

Copyright © 2017 Ana Gavrovska 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.

Abstract

There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. One of the major challenges is identifying primary heart sounds, S1 and S2, as they represent reference events for the analysis. The study presented in this paper analyzes the possibility of improving the structure characterization based on shape context and structure assessment using a small number of descriptors. Particularly, for the primary sound characterization, an adaptive waveform filtering is applied based on blanket fractal dimension for each preprocessed sound candidate belonging to pediatric subjects. This is followed by applying the shape based methods selected for the structure assessment of primary heart sounds. Different methods, such as the fractal ones, are used for the comparison. The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). The obtained results suggest that it is possible to improve the identification process using the shape related methods which are rarely applied. This can be helpful for applications involving automatic heart sound analysis.