Name
Title | ||
|---|---|---|
Pay More Attention With Fewer Parameters: A Novel 1-D Convolutional Neural Network for Heart Sounds Classification |
Abstract | ||
|---|---|---|
The cardiovascular disease (CVD) is one of the major causes of mortality worldwide. Auscultation of heart sounds or phonocardiograms (PCGs) analysis, which is an efficient and non-invasive way, has been shown to be promising and played an important role in preliminary CVD diagnosis. In this study, a deep learning-based PCG classification method is proposed, which is mainly comprised three steps: pre-processing, PCG patches classification using a novel 1-D deep convolutional neural network (CNN), and final predicting of PCG recordings based on the patch-level results. In order to maximize the information flow within the CNN, a block-stacked style architecture with clique blocks is employed, and in each clique block a bidirectional connection structure is utilized. Using the stacked blocks, the proposed CNN achieves both spatial and channel attention, which leads a superior classification performance. Besides, a novel separable convolution with inverted bottleneck is introduced to efficiently decouple features' dependency between spatial and channel-wise dependency of features. Experiments on PhysioNet/CinC 2016 reveal a superior classification performance and the advantage in parameter efficiency of the proposed method comparing to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.22489/CinC.2018.072 | 2018 Computing in Cardiology Conference (CinC) |
Keywords | Field | DocType |
CNN,PCG recordings,patch-level results,information flow,block-stacked style architecture,clique block,bidirectional connection structure,stacked blocks,spatial channel attention,efficiently decouple features,parameter efficiency,cardiovascular disease,mortality worldwide,auscultation,phonocardiograms analysis,deep learning-based PCG classification method,separable convolution,1D convolutional neural network,CVD diagnosis,heart sound classification,PCG patch classification | Bottleneck,Pattern recognition,Clique,Computer science,Convolutional neural network,Convolution,Communication channel,Artificial intelligence,Deep learning,Auscultation,Heart sounds | Conference |
Volume | ISSN | ISBN |
45 | 2325-8861 | 978-1-7281-0924-4 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunqiu Xu | 1 | 8 | 2.21 |
| Bin Xiao | 2 | 231 | 20.02 |
| Xiu-Li Bi | 3 | 134 | 6.37 |
| Weisheng Li | 4 | 141 | 29.73 |
| Junhui Zhang | 5 | 19 | 9.68 |
| Xu Ma | 6 | 21 | 4.12 |