title: A multi-spot exploration of the topological structures of the reconstructed phase-space for the detection of cardiac murmurs.
abstract: Acoustic heart signals are generated by a turbulence effect created when the heart valves snap shut, and therefore carrying significant information of the underlying functionality of the cardiovascular system. In this paper, we present a method for heart murmur classification divided into three major steps: a) features are extracted from the heart sound; b) features are selected using a Backward Feature Selection algorithm; c) signals are classified using a K-nearest neighbor's classifier. A new set of fractal features are proposed, which are based on the distinct signatures of complexity and self-similarity registered on the normal and pathogenic cases. The experimental results show that fractal features are the most capable of describing the non-linear structure and the underlying dynamics of heart sounds among the all feature families tested. The classification results achieved for the mitral auscultation spot (88% of accuracy) are in agreement with the current state of the art methods for heart murmur classification.
authors.forename: Jorge
authors.initials: J
authors.lastname: Oliveira
authors.forename: Cristina
authors.initials: C
authors.lastname: Oliveira
authors.forename: Bruna
authors.initials: B
authors.lastname: Cardoso
authors.forename: Malik Saad
authors.initials: MS
authors.lastname: Sultan
authors.forename: Miguel
authors.initials: M
authors.lastname: Tavares Coimbra
mesh_terms: D000465:Algorithms; D000068598:Data Accuracy; D006337:Heart Murmurs / Q000145:classification* / Q000175:diagnosis; D006801:Humans; D012815:Signal Processing, Computer-Assisted