A Review of Computer-Aided Heart Sound Detection Techniques

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Cardiovascular diseases (CVDs) are a leading global health concern.
• Heart sound analysis is a crucial noninvasive diagnostic tool for CVD prediction.
• Advancements in computational methods are enhancing heart sound detection capabilities.

Purpose of the Study:

• To review the latest developments in computer-aided heart sound detection techniques over the past five years.
• To provide insights into the application of deep learning in heart sound analysis.
• To identify future research directions for improved CVD prediction.

Main Methods:

• Review of theories on heart sounds and their relation to cardiovascular diseases.
• Analysis of key signal processing technologies: denoising, segmentation, feature extraction, and classification.
• Emphasis on the application of deep learning algorithms in heart sound processing.

Main Results:

• Recent progress in computer-aided heart sound detection has been systematically reviewed.
• Key signal processing techniques and their role in analyzing heart sounds are detailed.
• The significant impact and applications of deep learning in this field are highlighted.

Conclusions:

• Computer-aided heart sound detection is vital for noninvasive cardiovascular disease prediction.
• Deep learning shows great promise for advancing heart sound analysis.
• Further research is needed to refine these techniques for clinical application.