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A very desirable attribute in automatic pathology detection systems is noise robustness, such that the presence of noise should not significantly affect the ability to detect pathologies. This paper explores the discriminatory potential that nonlinear dynamic features, in particular, the Hurst exponent, the maximum Lyapunov exponent and correlation dimension, can provide in the detection of heart murmurs using phonocardiographic signals contaminated with different levels of noise. At the same time comparing with features obtained from time frequency representations. The results show the strength of features based on nonlinear dynamics for functional state classification tasks, even on signals with high noise levels.

Análisis de la tolerancia al ruido de características basadas en dinámica no lineal sobre señales fonocardiográficas