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Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption.
- Source :
-
Neurocomputing . Jun2020, Vol. 392, p153-159. 7p. - Publication Year :
- 2020
-
Abstract
- Automatic heart sound auscultation is one of the common used techniques for cardiovascular diseases detection. In this paper, a novel heart sound classification method based on deep learning technologies for cardiovascular disease prediction is introduced, which is mainly comprised three parts: pre-processing, 1-D waveform heart sound patches classification using a deep convolutional neural network (CNN) with attention mechanism, and majority voting for final prediction of heart sound recordings. In order to enhance the information flow of the CNNs, a block-stacked style architecture with clique blocks is employed, and in each clique block a bidirectional connection structure is introduced in the proposed CNN. By using the stacked clique and transition blocks, the proposed CNN achieves both spatial and channel attention leading a promising classification performance. Moreover, a novel separable convolution with inverted bottleneck is utilized to decouple the spatial and channel-wise relevancy of features efficiently. Experiments on PhysioNet/CinC 2016 show that the proposed method obtains a superior classification results and excels in consumption of parameter comparing to state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 392
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 143060012
- Full Text :
- https://doi.org/10.1016/j.neucom.2018.09.101