Exploratory study of the cardiac dynamic trajectory in the embedding space

In this paper, the topological and dynamical properties of the heart sounds are assessed. The signal is pre-processed and projected into an embedding subspace, which is more suitable to detect the irregularities and the unstable trajectories registered during the cardiac murmurs than the original heart sound signal. We present a method for heart murmur classification divided into five major steps: a) signal is divided into heart beats; b) entropy gradient envelogram is computed from the pre-processed signal; c) the orbital trajectories are reconstructed using the embedding theory; d) n orbits in the embedding subspace are extracted (per heart beat); e) the median of the n orbits is used as an input to K-Nearest Neighbors (KNN) classifier. The experimental results achieved are in agreement with the current state of art for heart murmur classification.