A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism

Frontiers in Physiology

Physiology

Pengfei Jiao¹  Zhidong Zhao¹  Yefei Zhang²  Zhixin Zhou²  Xianfei Zhang²  Yanjun Deng² 

[1] School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou, China;School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China;

关键词: fetal distress;    fetal heart rate;    lightweight model;    attention mechanism;    wavelet packet coefficient;   

DOI  :  10.3389/fphys.2023.1090937

 received in 2022-11-06, accepted in 2023-02-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model’s ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

【 授权许可】

Unknown   
Copyright © 2023 Deng, Zhang, Zhou, Zhang, Jiao and Zhao.