Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.
Journal:
Scientific reports
PMID:
40133506
Abstract
A heart murmur is an atypical sound produced by blood flow through the heart. It can indicate 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 different properties of heart sound signals. To address this issue, this study proposes a new discriminatory set of multiscale features based on the scaling and complexity properties of heart sounds, as characterized in the wavelet domain. Scaling properties are characterized by examining 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 classifiers. When applied to a publicly available heart sound dataset, our proposed wavelet-based multiscale features achieved 76.61% accuracy using support vector machine classifier, demonstrating competitive performance with existing deep learning methods while requiring significantly fewer features. This suggests that scaling nature and complexity properties in heart sounds could be potential biomarkers for improving the accuracy of murmur detection.