生物医学工程研究2025，Vol.44Issue(6)：387-394,8.DOI:10.19529/j.cnki.1672-6278.2025.06.06
基于全维动态卷积与特征融合的心脏杂音等级识别
Heart murmur grade recognition based on omni-dimensional dynamic convolution and feature fusion
摘要
Abstract
To address the issue of subjectivity and susceptibility to environmental interference in murmur grading diagnosis through manual auscultation,we constructed a heart murmur grade recognition model based on omni-dimensional dynamic convolution(ODConv)and feature fusion.Firstly,the ODConv module was adopted to capture rich context information.Secondly,time-domain and time-frequency domain features were integrated to improve the recognition performance of murmur levels.Finally,the experiments on the amplitude attenuation and enhancement of the first heart sound(S1)and the second heart sound(S2)were designed to verify the influence of the relative loudness of S1,S2 and the murmur on the murmur level recognition of the model.The verification results on the CirCor DigiScope dataset showed that this model not only referred to the relative loudness information of the murmur with S1 and S2 in the classification of murmur levels,but also achieved recall rate of 93.39%,53.70%and 73.46%in the Absent,Soft and Loud cate-gories,as well as F1 score of 94.07%,51.28%and 73.34%,respectively.The proposed model can accurately identify heart murmur severity,and provide crucial technical support for the automated recognition of heart murmur levels.关键词
深度学习/特征融合/心脏杂音分级/对数耳蜗谱Key words
Deep learning/Feature fusion/Heart murmur grading/Logarithmic gammatone spectrums分类
医药卫生引用本文复制引用
首都,黄昭涵,李虎浩,李莉,赵启军,潘帆..基于全维动态卷积与特征融合的心脏杂音等级识别[J].生物医学工程研究,2025,44(6):387-394,8.基金项目
国家自然科学基金项目(62066042) （62066042）
四川省重点研发项目(2024YFFK0051). （2024YFFK0051）
