Do we really need a segmentation step in heart sound classification algorithms?

Area of Science:

• Cardiology
• Biomedical Signal Processing
• Machine Learning in Healthcare

Background:

• Cardiac auscultation is a primary method for detecting cardiovascular diseases (CVDs).
• Automated CVD detection relies on analyzing heart sounds, with segmentation (heart sound boundary detection) being a critical step.
• The necessity of a dedicated heartbeat alignment step within the signal classification pipeline is currently debated.

Purpose of the Study:

• To evaluate the impact of a heartbeat alignment step on the performance of various machine learning algorithms for cardiovascular disease detection.
• To determine if advanced algorithms can effectively detect heart murmurs without explicit segmentation.

Main Methods:

• Comparison of machine learning algorithms, including deep learning models (Gate Recurrent Unit Network, Convolutional Neural Network) and traditional methods (Support Vector Machine, Random Forest).
• Evaluation of algorithm performance with and without a preceding heartbeat alignment/segmentation step.
• Assessment of the ability to detect heart murmurs and heart sounds.

Main Results:

• Gate Recurrent Unit (GRU) Network and Convolutional Neural Network (CNN) demonstrated robustness, accurately detecting heart murmurs even without a heartbeat alignment step.
• Support Vector Machine (SVM) and Random Forest (RF) algorithms showed a performance decrease of approximately 5% when the explicit segmentation step was omitted.
• These findings indicate that advanced deep learning models may obviate the need for traditional segmentation in certain cardiac auscultation analysis tasks.

Conclusions:

• Deep learning models like GRU and CNN offer a more streamlined approach to heart sound analysis for CVD screening, potentially reducing computational steps.
• Traditional machine learning models (SVM, RF) still benefit significantly from precise heart sound segmentation for accurate murmur detection.
• The study highlights the evolving role of segmentation in automated cardiac auscultation, suggesting a shift towards segmentation-free analysis with advanced AI.