Feature extraction for murmur detection based on support vector regression of time-frequency representations

This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonoc...

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Bibliographic Details
Published in:2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008-01, p.1623-1626
Main Authors: Jaramillo-Garzon, J., Quiceno-Manrique, A., Godino-Llorente, I., Castellanos-Dominguez, C.G.
Format: Article
Language:English
Subjects:
Feature extraction
Kernel
Phonocardiography
Prototypes
Support vector machines
Time-frequency analysis
Training
Online Access:Request full text
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Summary:This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2008.4649484