AI-Enhanced Detection of Heart Murmurs: Advancing Non-Invasive Cardiovascular Diagnostics
Maria-Alexandra Zolya1, Elena-Laura Popa1, Cosmin Baltag1
1Department of Automatics and Information Technology, Transilvania University of Brasov, 500036 Brasov, Romania.
Insights
A new AI model accurately detects heart murmurs from sound recordings, improving early diagnosis of cardiovascular diseases. This non-invasive technology offers accessible cardiac diagnostics, especially for underserved regions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality, with over 17 million deaths annually.
- Early detection of heart murmurs, indicative of valve abnormalities, is crucial for patient outcomes.
- Current diagnostic methods face limitations in accessibility, cost, and invasiveness, particularly in resource-limited settings.
Purpose of the Study:
- To develop and evaluate a novel Convolutional Recurrent Neural Network (CRNN) model for non-invasive heart murmur classification.
- To assess the model's performance in accurately identifying heart murmurs from audio recordings.
- To explore the potential of AI in enhancing cardiac diagnostics and accessibility.
Main Methods:
- Utilized a CRNN model integrating convolutional and recurrent layers to process heart sound recordings.
- Applied pre-processing techniques including z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, pitch shift).
- Trained and validated the model on heart sound datasets to capture spatial and temporal audio features.
Main Results:
- The CRNN model achieved high diagnostic performance with 90.5% accuracy.
- The model demonstrated strong precision (89%) and recall (87%) in heart murmur classification.
- The study highlights the model's robustness and effectiveness in analyzing cardiac auscultation data.
Conclusions:
- The developed CRNN model shows significant potential for revolutionizing cardiac diagnostics through non-invasive heart murmur detection.
- This AI-driven approach offers a scalable and accessible solution for early identification of cardiovascular conditions.
- The technology holds promise for broader applications in healthcare, especially in regions with limited access to traditional diagnostic resources.
Abstract:
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, claiming over 17 million lives annually. Early detection of conditions like heart murmurs, often indicative of heart valve abnormalities, is critical for improving patient outcomes. Traditional diagnostic methods, including physical auscultation and advanced imaging techniques, are constrained by their reliance on specialized clinical expertise, inherent procedural invasiveness, substantial financial costs, and limited accessibility, particularly in resource-limited healthcare environments. This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. The model processes heart sound recordings using advanced pre-processing techniques such as z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, and pitch shift) to enhance robustness. By combining convolutional and recurrent layers, the CRNN captures spatial and temporal features in audio data, achieving an accuracy of 90.5%, precision of 89%, and recall of 87%. These results underscore the potential of machine-learning technologies to revolutionize cardiac diagnostics by offering scalable, accessible solutions for the early detection of cardiovascular conditions. This approach paves the way for broader applications of AI in healthcare, particularly in underserved regions where traditional resources are scarce.
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