Intelligent classification of cardiotocography based on a support vector machine and convolutional neural network: Multiscene research.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Published Date: Nov 27, 2023

Abstract

OBJECTIVE: To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently.

Authors

  • Wen Zhang
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences Wuhan 430062 China peiwuli@oilcrops.cn zhangqi521x@126.com +86-27-8681-2943 +86-27-8671-1839.
  • Zixiang Tang
    Wuhan Second Ship Design and Research Institute, Wuhan, Hubei, China.
  • Huikai Shao
    School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Chao Sun
    Hospital for Skin Diseases and Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
  • Xin He
    Department of Nephrology, The Affiliated Hospital of Guizhou Medical, Guizhou, China.
  • Jiahui Zhang
    Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
  • Tiantian Wang
  • Xiaowei Yang
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China.
  • Yiran Wang
    College of Computer Science and Technology, Qingdao University, Qingdao, Shandong Province, China.
  • Yadi Bin
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Lanbo Zhao
    From the Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi, China (Sun, Zhao, Han, Ma, Liang, L. Wang, Tuo, Zhang, Li).
  • Siyi Zhang
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Dongxin Liang
    From the Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi, China (Sun, Zhao, Han, Ma, Liang, L. Wang, Tuo, Zhang, Li).
  • Jianliu Wang
    Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China.
  • Dexing Zhong
    School of Automation Science and Engineering, Xi'an Jiaotong University Xi'an, Shannxi, China (R. Wang, Zhong).
  • Qiling Li

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