Heart Sound Classification using the Nonlinear Dynamic Feature Approach along with Conventional Classifiers

Abstract

Heart sounds show chaotic and complex behavior when murmurs are present, containing nonlinear and non-Gaussian information. This paper studies ways to extract features from nonlinear dynamic models. The features frequently used to describe the underlying dynamics of the heart are derived from nonlinear dynamical modeling of heart sound signals. This study incorporates nonlinear dynamic features alongside conventional classifiers in the analysis of phonocardiograms (PCGs), achieving a significant improvement in the classification performance with 0.90 sensitivity and 0.92 specificity.