A review on deep learning methods for heart sound signal analysis

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• Heart sound analysis is crucial in biomedical engineering.
• Deep Learning (DL) methods offer promising tools for this analysis.
• Methodological diversity in DL for heart sounds creates performance uncertainties.

Purpose of the Study:

• To survey recent advances in heart sound analysis using Deep Learning methods.
• To provide a realistic picture of methodological performance in DL-based heart sound analysis.
• To compare DL methods based on methodological and applicative taxonomies.

Main Methods:

• Conducted a broad retrospective study on DL methods for heart sound analysis.
• Utilized well-known search engines to cover a wide span of related keywords.
• Represented and compared implemented methods and their results.

Main Results:

• Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are most common for heart sound classification and localization.
• CNNs and Autoencoder networks achieved 100% accuracy in classifying abnormal vs. normal heart sounds in case studies.
• Evaluation inconsistencies limit definitive conclusions on method superiority.

Conclusions:

• DL methods, particularly CNNs and RNNs, are increasingly vital for heart sound analysis.
• While high accuracies are reported, standardized evaluation is needed for reliable performance assessment.
• Further research should focus on consistent evaluation metrics for DL in cardiovascular diagnostics.