一种通过增强图像编码和非对称卷积网络的心音分类算法

doi:10.11896/jsjkx.240700195

Abstract

Abstract: This paper proposes a heart sound classification algorithm using enhanced image coding and asymmetric convolutional networks.Unlike traditional methods that extract heart sounds based on statistical features and time-frequency domain features,this algorithm enhances three image coding methods—Gramian angular field(GAF),Markov transition field(MTF),and recurrence plot(RP)—by introducing fractional Fourier transform(FrFT),which constitutes the image coding modules of FrFT-GAF,FrFT-MTF,and FrFT-RP,respectively.The one-dimensional heart sound signal is transformed into a two-dimensional encoded feature map using these image coding modules.An asymmetric convolutional network(ACNet) leverages computer vision advantages to analyze and process the two-dimensional encoded feature map for effective heart sound classification.In addition,the performance of the above image coding modules is evaluated and compared respectively.Experimental results demonstrate that the FrFT-RP module achieves the best classification performance in binary heart sound classification tasks,with an accuracy of 0.981 and 0.977,and F1 score of 0.989 and 0.974 on dataset 1 and dataset 2(Physio Net/CinC 2016 dataset),respectively.The FrFT-MTF and FrFT-GAF modules show effective performance in that order.The performance of the method using FrFT to enhance image encoding features has significantly improved compared to previous methods,providing novel approaches and methods for heart sound signal classification,is expected to be applied in machine assisted diagnosis of congenital heart disease.

Key words: Congenital heart disease, Heart sound, Image coding, Fractional Fourier transform, Asymmetric convolutional network

CLC Number:

TN912

WANG Shengyi, YANG Hongbo, PAN Jiahua, WANG Weilian. Heart Sound Classification Algorithm Using Enhanced Image Coding and AsymmetricConvolutional Networks[J].Computer Science, 2025, 52(6A): 240700195-8.

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Heart Sound Classification Algorithm Using Enhanced Image Coding and AsymmetricConvolutional Networks

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