Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (9): 62-70.doi: 10.11896/jsjkx.250100102

• Intelligent Medical Engineering • Previous Articles     Next Articles

Application of End-to-End Convolutional Kolmogorov-Arnold Networks in Atrial Fibrillation Heart Sound Recognition

DENG Hong1, CHEN Yan2, YANG Hongbo3, ZHAO Feng2, JIANG Yongzhuo1, GUO Tao3, WANG Weilian1   

  1. 1 School of Information Science and Engineering,Yunnan University,Kunming 650504,China
    2 The First Medical Center of Chinese PLA General Hospital,Beijing 100853,China
    3 Fuwai Yunnan Hospital,Chinese Academy of Medical Sciences,Kunming 650102,China
  • Received:2025-01-15 Revised:2025-04-13 Online:2025-09-15 Published:2025-09-11
  • About author:DENG Hong,born in 2000,postgra-duate.His main research interests include biomedical signal processing and deep learning.
    WANG Weilian,born in 1947,professor.His main research interests include biological signal processing,digital-analog hybrid IC and ASIC design.
  • Supported by:
    National Natural Science Foundation of China(82172185,81960067) and Major Science and Technology Projects of Yunnan Province in 2018(2018ZF017).

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Abstract: Atrial fibrillation(AF) is a severe cardiac arrhythmia that requires early diagnosis.Traditional diagnostic methods typically involve cardiologists using electrocardiograms(ECG) and echocardiograms to make diagnostic conclusions.To address issues such as high costs,excessive reliance on clinical expertise,and limited accessibility,this study proposes an innovative application of Kolmogorov-Arnold Networks(KAN) in AF heart sound analysis.This study explores the application of convolutional KAN(CKAN) in AF heart sound recognition,proposing an end-to-end AF identification framework based on CKAN architecture,which incorporates flexible linear activation functions and exhibits superior parameter efficiency.To enhance the usability of heart sound signals,the methodology first applies preprocessing,including signal segmentation,quality assessment,and data cleansing.Subsequently,the model autonomously learns discriminative features through KAN-based convolutional and pooling layers.Fina-lly,a CKAN-based classifier is employed for classification.During the feature extraction phase,self-attention mechanisms and focus modulation are incorporated into CKAN to efficiently extract signal features.In the classification phase,CKAN’s bottleneck structure and regularization techniques are explored to improve the model’s recognition performance.The proposed model is evaluated on a heart sound dataset from The First Medical Center of Chinese PLA General Hospital,achieving an accuracy of 97.86%,sensitivity of 98.18%,specificity of 97.43%,and an Fβ score of 98.06%.The results indicate that the CKAN model provides significant advantages in aiding the diagnosis of AF from heart sound signals.

Key words: Atrial fibrillation, Convolutional KAN, Heart sound recognition, End-to-End, Data cleansing

CLC Number: 

  • TN912
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