NDLI: Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms

Content Provider	IEEE Xplore Digital Library
Author	Pedrosa, J. Castro, A. Vinhoza, T.T.V.
Copyright Year	2014
Description	Author affiliation: Fac. de Eng., Uni-versidade do Porto, Porto, Portugal (Pedrosa, J.; Castro, A.) \|\| Dept. de Cienc. de Comput., Univ. do Porto, Porto, Portugal (Vinhoza, T.T.V.)
Abstract	The digital analysis of heart sounds has revealed itself as an evolving field of study. In recent years, numerous approaches to create decision support systems were attempted. This paper proposes two novel algorithms: one for the segmentation of heart sounds into heart cycles and another for detecting heart murmurs. The segmentation algorithm, based on the autocorrelation function to find the periodic components of the PCG signal had a sensitivity and positive predictive value of 89.2% and 98.6%, respectively. The murmur detection algorithm is based on features collected from different domains and was evaluated in two ways: a random division between train and test set and a division according to patients. The first returned sensitivity and specificity of 98.42% and 97.21% respectively for a minimum error of 2.19%. The second division had a far worse performance with a minimum error of 33.65%. The operating point was chosen at sensitivity 69.67% and a specificity 46.91% for a total error of 38.90% by varying the percentage of segments classified as murmurs needed for a positive murmur classification.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2294
Ending Page	2297
File Size	702898
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944078
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Feature extraction Phonocardiography Algorithm design and analysis Sensitivity Databases Accuracy
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Health Informatics Signal Processing Biomedical Engineering

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