Paper Info
Title
Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms.
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.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944078
EMBC
Keywords
Field
DocType
total error,heart murmur detection algorithm,paediatrics,positive murmur classification,pcg signal,autocorrelation function,murmur detection,medical signal processing,cardiovascular system,phonocardiography,decision support systems,signal classification,periodic components,pediatric phonocardiograms,operating point,automatic heart sound segmentation,digital analysis
Computer vision,Computer science,Segmentation,Electronic engineering,Speech recognition,Artificial intelligence
Conference
Volume
ISSN
Citations 
2014
1557-170X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Joao Pedrosa182.89
Ana Castro242.83
T. T. V. Vinhoza319413.52