Key result
CNN analyzing phonocardiography signals distinguishes normal from pathological heart sounds with ~90% accuracy.
Why the study?
Cardiovascular diseases remain the largest cause of death worldwide, making precise and effective diagnostic techniques essential.
Does a CNN-based automated analysis of PCG signals accurately identify pathological cardiac sounds?
Does a CNN-based automated analysis of PCG signals accurately identify pathological cardiac sounds?
A CNN-based automated analysis of PCG signals can distinguish between normal and pathological heart sounds with >90% accuracy, offering a potential tool for early detection of cardiovascular diseases.
Hypothesis-generating for CNN-based PCG screening; prospective validation needed before clinical adoption.
Since cardiovascular diseases (CVDs) continue to be the largest cause of death worldwide, precise and effective diagnostic techniques are essential. Phonocardiography (PCG), which records minute heart sounds, has the potential to identify cardiac defects early. In order to automate the detection of heart abnormalities, this study explores the use of convolutional neural networks (CNNs) in PCG signal analysis. Five different kinds of heart sounds were pre-processed from a large dataset. To extract complex characteristics from PCG signals, a sophisticated CNN architecture was developed that consists of numerous convolutional and pooling layers. The model was able to identify between normal and pathological cardiac sounds with an accuracy of more than 90 %. Techniques for data normalisation, augmentation, and visualisation were used to improve training effectiveness and validate the model. This study provides an ethical and effective method of identifying circulatory irregularities at an early stage, demonstrating the promise of AI in transforming cardiac diagnostics.
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Dwivedi et al. (2024) studied Cardiovascular diseases. CNN-based automated analysis of PCG signals was evaluated on Accuracy of identifying between normal and pathological cardiac sounds. A convolutional neural network (CNN) architecture analyzing phonocardiography signals successfully distinguished between normal and pathological cardiac sounds with an accuracy of more than 90%.
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