残差网络与注意力机制结合的啰音检测方法  

Rale Detection Method Based on Residual Network and Attention Mechanism

作　　者：杨淋坚 张宇[1] Yang Linjian;Zhang Yu(Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学,广东广州510006

出　　处：《自动化与信息工程》2021年第1期31-34,40,共5页Automation & Information Engineering

摘　　要：为解决啰音强度和性质易改变而导致的支持向量机人工参数选择困难、检测精度不高等问题,提出一种残差网络与注意力机制结合的啰音检测方法。通过残差网络加深网络结构提取更多层次的信息,同时加入注意力机制进一步挖掘通道层面与空间维度特征,实现啰音检测。使用自主研发的数字听诊器记录的呼吸音进行实验。实验结果表明:本文提出的方法相较于SVM和ResNet50啰音检测精度分别提高了6.83%和1.58%。In order to solve the problems caused by the easy change of rales intensity and properties, such as difficulty in selecting artificial parameters of support vector machine, poor detection accuracy and so on. A rales detection method based on residual network and attention mechanism is proposed. Through the residual network to deepen the network structure to extract more levels of information, while adding the attention mechanism to further mine the channel level and spatial dimension features to achieve rales detection. We used a self-developed digital stethoscope to record a total of 2620 breath sounds in 325 subjects. The experimental results show that compared with SVM and ResNet50, the proposed method improves the accuracy of rale detection by 6.83% and 1.58% respectively.

关 键 词：啰音检测 信号处理 残差网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]