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Detecting Premature Ventricular Contraction in Children with Deep Learning

  • Yixiu Liu (刘宜修)
  • Yujuan Huang (黄玉娟)
  • Jianyi Wang (王健怡)
  • Li Liu (刘 莉)
  • Jiajia Luo (罗家佳)
Article

Abstract

Premature ventricular contractions (PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram (ECG), which is onerous and time-consuming. In this study, deep learning was applied to develop models that can detect PVCs in children automatically. This computer-aided diagnosis model achieved high accuracy while sustained stable performance. It could save time and repeated efforts for physicians, enabling them to focus on more complicated tasks.This study is a first step toward children’s PVC auto-detection in clinics. Further study will improve the model’s performance with optimized structure and more data in different sources, while facing the challenges of the variety and uncertainty of children’s ECG with heart diseases.

Key words

premature ventricular contraction pediatrics deep learning convolutional neural network heart disease 

CLC number

TP 39 

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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yixiu Liu (刘宜修)
    • 1
  • Yujuan Huang (黄玉娟)
    • 2
  • Jianyi Wang (王健怡)
    • 2
  • Li Liu (刘 莉)
    • 2
  • Jiajia Luo (罗家佳)
    • 1
  1. 1.University of Michigan - Shanghai Jiao Tong University Joint-InstituteShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Children’s Hospital of ShanghaiShanghai Jiao Tong UniversityShanghaiChina

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