Classification of Heart Sounds Using Convolutional Neural Network
Li, F., Tang, H., Shang, S., Mathiak, K., & Cong, F. (2020). Classification of Heart Sounds Using Convolutional Neural Network. Applied Sciences, 10(11), Article 3956. https://doi.org/10.3390/app10113956
Published inApplied Sciences
Li, Fan |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity. ...
ISSN Search the Publication Forum2076-3417
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Additional information about fundingThis research was funded by the National Natural Science Foundation of China, grant number 91748105 & 81471742; the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China.
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