Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Heart sound auscultation is crucial for diagnosing cardiac conditions but requires expert interpretation.
• Deep learning (DL) shows promise in analyzing complex heart sound patterns, with growing data availability.
• Existing reviews lack comprehensive summaries of DL in heart sound analysis, focusing on data and clinical applications.

Purpose of the Study:

• To review available heart sound datasets for DL model training.
• To introduce DL fundamentals and state-of-the-art techniques in heart sound analysis.
• To summarize current clinical applications of DL in heart sound analysis, including limitations and future directions.

Main Methods:

• Systematic review of literature on deep learning for heart sound analysis.
• Compilation and categorization of publicly available heart sound datasets.
• Analysis of DL algorithms and their performance in clinical applications.

Main Results:

• Detailed overview of commonly used heart sound datasets and their characteristics.
• Summary of DL techniques applied to heart sound analysis, including convolutional neural networks and recurrent neural networks.
• Exploration of DL applications in detecting various cardiac conditions, such as murmurs and arrhythmias.

Conclusions:

• Deep learning integration significantly advances heart sound analysis and clinical practice.
• Increasing data and improved DL techniques facilitate wider clinical adoption of these models.
• Further research is essential to overcome challenges and enhance the clinical utility of DL in cardiology.