基于两级神经网络的心音分割  

Heart Sound Segmentation Based on Two-Stage Neural Network

作　　者：冯正伟 全海燕[1] FENG Zhengwei;QUAN Haiyan(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650031,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650031

出　　处：《数据采集与处理》2023年第4期849-859,共11页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61861023)。

摘　　要：心音信号是分析诊断心脏疾病的重要信号,而心音分割是对其进行分析处理之前必不可少的一步。本文通过将心音分割任务分离为定位与识别两个子任务,提出一种两级卷积神经网络,由定位网络和判别网络两级构成,分别完成心音信号的识别与定位。首先将原始信号通过滑动窗口进行分帧,然后通过短时傅里叶变换得到其频谱,再通过梅尔滤波器得到其梅尔频谱系数(Mel frequency spectral coefficient,MFSC)特征,输入第1个定位网络对其是否为心音段进行判断,如果是的话,再输入判别神经网络,识别第一心音与第二心音,从而实现心音的分割。最后利用多帧结果投票,减小误判。同时,在卷积神经网络中引入空间注意力机制,实验结果表明,这种加入了注意力机制的两级神经网络模型在心音分割任务上比使用单个卷积神经网络分类模型的准确率更高,也使得模型更加简单,轻量化。Heart sound signal is an important signal for analyzing and diagnosing heart problems,and heart sound segmentation is an essential part before analyzing and processing it.By separating the heart sound segmentation task into two sub tasks of localization and recognition,this paper proposes a two-stage convolutional neural network,which is composed of localization network and discrimination network to complete the recognition and localization of heart sound signals respectively.First,the original signal is divided into frames through a sliding window,then the spectrum is obtained by short time Fourier transform,and then the Mel frequency spectral coefficient(MFSC)characteristics are obtained by Mel filter.The first localization model is input to judge whether it is a heart sound segment.If so,the discrimination neural network is input to identify the first heart sound and the second heart sound,so as to achieve heart sound segmentation.At last,multi frame voting results are used to reduce the misjudgment.At the same time,the spatial attention mechanism is introduced into the convolutional neural network.Experimental results show that this two-stage neural network model with attention mechanism has higher accuracy in heart sound segmentation tasks than a single convolutional neural network classification model,and also makes the model more simple and lightweight.

关 键 词：心音分割 短时傅里叶变换 梅尔倒谱 卷积神经网络 空间注意力机制 

分 类 号：R318.04[医药卫生—生物医学工程]