Deep Learning Methods for Heart Sounds Classification: A Systematic Review

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for hear...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-05, Vol.23 (6), p.667
Main Authors: Chen, Wei, Sun, Qiang, Chen, Xiaomin, Xie, Gangcai, Wu, Huiqun, Xu, Chen
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Artificial intelligence
Artificial neural networks
Auscultation
Classification
CNN
CVDs
Deep learning
Electrocardiography
Heart
heart sounds classification
Machine learning
Neural networks
Physiology
Recurrent neural networks
Review
RNN
Sound
Systematic review
Wavelet transforms
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Summary:The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23060667