Paper Info
Title
Heart Murmur Classification Using Complexity Signatures
Abstract
In this work, we propose a two-stage classifier based on the analysis of the heart sound’s complexity for murmur identification and classification. The first stage of the classifier verifies if the heart sound (HS) exhibits murmurs. To this end, the chaotic nature of the signal is assessed using the Lyapunov exponents (LEs). The second stage of the method is devoted to the classification of the type of murmur. In opposition to current state of the art methods for murmur classification, a reduced set of features is proposed. This set includes both well-known as well as new features designed to capture the morphological and the chaotic nature of murmurs. The classification scheme is evaluated with three classification methods: Learning Vector Quantization, Gaussian Mixture Models and Support Vector Machines. The achieved results are comparable to reported results in literature, while relying on a significant smaller set of features.
Year
DOI
Venue
2010
10.1109/ICPR.2010.628
Pattern Recognition
Keywords
Field
DocType
Lyapunov methods,acoustic signal processing,cardiology,digital signatures,feature extraction,medical signal processing,quantisation (signal),support vector machines,Gaussian mixture models,Lyapunov exponents,classification scheme,complexity signatures,heart murmur classification,heart sound,learning vector quantization,support vector machines,Lyapunov exponent,feature extraction,heart murmur
Pattern recognition,Heart murmur,Computer science,Support vector machine,Learning vector quantization,Feature extraction,Speech recognition,Artificial intelligence,Chaotic,Artificial neural network,Classifier (linguistics),Mixture model
Conference
ISSN
ISBN
Citations 
1051-4651
978-1-4244-7542-1
4
PageRank 
References 
Authors
0.55
1
4
Name
Order
Citations
PageRank
Kumar, D.140.55
Paulo Carvalho225047.68
Ricardo Couceiro33510.16
Manuel Antunes4449.87