A heart murmur grading method based on lightweight convolutional neural networks
HUANG Zhaohan
HE Peiyu
LI Shilong
LI Li
ZHAO Qijun
PAN Fan
Abstract：Aiming at the issues of experience dependence and subjectivity in artificial auscultation,a heart murmur grading method based on lightweight convolutional neural networks was proposed.Firstly,the sliding window method and Gammatone filter bank were used to preprocess the heart sound signal,and the log-cochleagram of the heart sound fragment was obtained,which was used as the in-put feature of the network.Secondly,the initial convolutional module and selective convolutional module were designed to capture glob-al features and multi-scale features,and the depth-separable convolution was used to reduce the number of network parameters.Final-ly,based on the proposed decision rules,the prediction results of multiple auscultation sites were combined to obtain the heart murmur grade of patients.The validation experiment results on the CirCor DigiScope PCG dataset showed that the unweighted average recall rate,weighted average recall rate and unweighted F1 score on the test set reached 81.63％,88.46％and 79.85％,respectively.This method has better performance of murmur grading,which is not only suitable for terminal equipment,but also provides an important ba-sis for automatic analysis of heart disease.
Keywords：Heart murmurMurmur gradingLog-cochleagramLightweight networkSelective kernel convolutionClinical decision
Publication Date：2024-12-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：9( 423-431 )