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

TL;DR

This paper introduces a novel wavelet-based multiscale feature set leveraging self-similarity and complexity properties of heart sounds to improve murmur detection, achieving comparable results with fewer features.

Contribution

It proposes a new set of multiscale features based on self-similarity and complexity in heart sounds, enhancing murmur detection with fewer features.

Findings

Features achieved comparable performance to existing methods.

Self-similarity and complexity can serve as potential biomarkers.

Fewer features are needed for effective detection.

Abstract

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.