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

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.