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

A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties