  A heart murmur is an atypical sound produced by the flow of blood through the
heart. It can be a sign of a serious heart condition, so detecting heart
murmurs is critical for identifying and managing cardiovascular diseases.
However, current methods for identifying murmurous heart sounds do not fully
utilize the valuable insights that can be gained by exploring intrinsic
properties of heart sound signals. To address this issue, this study proposes a
new discriminatory set of multiscale features based on the self-similarity and
complexity properties of heart sounds, as derived in the wavelet domain.
Self-similarity is characterized by assessing fractal behaviors, while
complexity is explored by calculating wavelet entropy. We evaluated the
diagnostic performance of these proposed features for detecting murmurs using a
set of standard classifiers. When applied to a publicly available heart sound
dataset, our proposed wavelet-based multiscale features achieved comparable
performance to existing methods with fewer features. This suggests that
self-similarity and complexity properties in heart sounds could be potential
biomarkers for improving the accuracy of murmur detection.
