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Improving the Detection Performance of Cardiovascular Diseases from Heart Sound Signals with a New Deep
Ozgen Safak1, Mehmet Tolga Hekim1, Tolga Cakmak2
1Clinics of Cardiology, Balıkesir University, Balıkesir 10185, Turkey.
Insights
This study presents an AI-powered system for diagnosing cardiovascular diseases using heart sound analysis. The novel approach achieves over 98% accuracy, enabling early detection and reducing mortality risk.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Signal Processing
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality.
- Early diagnosis of CVDs is crucial for mitigating adverse outcomes.
- Traditional heart sound auscultation requires expert interpretation, limiting accessibility.
Purpose of the Study:
- To develop an artificial intelligence (AI)-based decision support system for diagnosing cardiovascular diseases.
- To leverage phonocardiogram (PCG) signals for automated CVD detection.
- To enhance the accuracy and reliability of heart sound analysis.
Main Methods:
- Utilized the 2016 PhysioNet/CinC Challenge dataset of PCG signals.
- Applied spectrogram image transformation for enhanced signal representation.
- Employed a deep learning model (residual and attention blocks, MLP-mixer) for feature extraction.
- Developed a hybrid feature selection algorithm (NCA and ReliefF).
- Classified features using a Support Vector Machine (SVM) algorithm.
Main Results:
- The proposed AI system achieved over 98% accuracy in diagnosing cardiovascular diseases.
- High performance was consistently observed across all evaluated metrics: accuracy, sensitivity, specificity, precision, and F1-score.
- The combined feature selection and deep learning approach proved effective.
Conclusions:
- An accurate AI-based decision support system for cardiovascular disease detection has been successfully developed.
- The system demonstrates high potential for improving early diagnosis and patient outcomes.
- This AI approach offers a scalable and reliable method for analyzing heart sounds.
Abstract:
Background/Objectives: Cardiovascular diseases are among the leading causes of death worldwide. Early diagnosis of these conditions minimizes the risk of future death. Listening to heart sounds with a stethoscope is one of the easiest and fastest methods for diagnosing heart conditions. While heart sounds are a quick and easy diagnostic method, they require significant expert interpretation. Recently, artificial intelligence models trained based on these expert interpretations have become popular in the development of decision support systems. Methods: The proposed approach uses the popular 2016 PhysioNet/CinC Challenge dataset for PCG signals. Spectrogram image transformation was then performed to increase the representativeness of these signals. A deep learning-based model that allows for the simultaneous training of residual and attention blocks and the MLP-mixer model was used for feature extraction. A new algorithm combining the strengths of NCA and ReliefF algorithms was proposed to select the strongest features in the feature set. The SVM algorithm was used for classification. Results: With this proposed approach, over 98% success was achieved in all accuracy, sensitivity, specificity, precision, and F1-score metrics. Conclusions: As a result, an artificial intelligence-based decision support system that detects cardiovascular diseases with high accuracy is presented.
Related Concept Videos
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