计算机科学
卷积神经网络
块(置换群论)
模式识别(心理学)
深度学习
卷积(计算机科学)
人工智能
集团
瓶颈
频道(广播)
语音识别
波形
人工神经网络
电信
数学
嵌入式系统
组合数学
雷达
几何学
作者
Bin Xiao,Yunqiu Xu,Xiuli Bi,Junhui Zhang,Xu Ma
标识
DOI:10.1016/j.neucom.2018.09.101
摘要
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.
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