Key result
Transfer learning models classify phonocardiogram heart diseases with 98% accuracy, outperforming traditional approaches.
Why the study?
Physician auscultation skills vary and misdiagnoses occur, creating a need for accurate computational tools using artificial intelligence to assist auscultation for cardiovascular disease detection.
Transfer learning models applied to continuous wavelet transform spectrograms of heart sounds can accurately classify six categories of normal and abnormal heart sounds, offering a potential tool for computer-assisted auscultation.
BACKGROUND AND AIMS: Auscultation is a cheap and fundamental technique for detecting cardiovascular disease effectively. Doctors' abilities in auscultation are varied. Sometimes, there may be cases of misdiagnosis, even when auscultation is performed by an experienced doctor. Hence, it is necessary to propose accurate computational tools to assist auscultation, especially in developing countries. Artificial intelligence technology can be an efficient diagnostic tool for detecting cardiovascular disease. This work proposed an automatic multiple classification method for cardiovascular disease detection by heart sound signals. METHODS AND RESULTS: In this work, a 1D heart sound signal is translated into its corresponding 3D spectrogram using continuous wavelet transform (CWT). In total, six classes of heart sound data are used in this experiment. We combine an open database (including five classes of heart sound data: aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse and normal) with one class (pulmonary hypertension) of heart sound data collected by ourselves to perform the experiment. To make the method robust in a noisy environment, the background deformation technique is used before training. Then, 10 transfer learning networks (GoogleNet, SqueezeNet, DarkNet19, MobileNetv2, Inception-ResNetv2, DenseNet201, Inceptionv3, ResNet101, NasNet-Large, and Xception) are used for comparison. Furthermore, other models (LSTM and CNN) are also compared with our proposed algorithm. The experimental results show that four transfer learning networks (ResNet101, DenseNet201, DarkNet19 and GoogleNet) outperformed their peer models with an accuracy of 0.98 to detect the multiple heart diseases. The performances have been validated both in the original heart sound and the augmented heart sound using 10-fold cross validation. The results of these 10 folds are reported in this research. CONCLUSIONS: Our method obtained high classification accuracy even under a noisy background, which suggests that the proposed classification method could be used in auxiliary diagnosis for cardiovascular diseases.
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Wang et al. (2022) studied Cardiovascular disease (valvular heart disease and pulmonary hypertension). Transfer learning models (e.g., ResNet101, GoogleNet) vs. Simple CNN and Bi-LSTM networks was evaluated on Classification accuracy. Transfer learning networks, including ResNet101 and GoogleNet, outperformed traditional models by achieving 98% accuracy in classifying six categories of heart diseases from phonocardiogram recordings.
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