An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.

Journal: Computer methods and programs in biomedicine
Published Date: Nov 14, 2019

Abstract

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a type of arrhythmia with high incidence. Automatic AF detection methods have been studied in previous works. However, a model cannot be used all the time without any improvement. And updating model requires adequate data and cost. Therefore, this study aims at finding a low-cost way to choose learning samples and developing an incremental learning system for AF detection.

Authors

  • Haotian Shi
    School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.
  • Haoren Wang
    School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.
  • Chengjin Qin
    School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.
  • Liqun Zhao
    Department of Cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, 100, Haining Road, Shanghai 200080, PR China.
  • Chengliang Liu
    School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China. Electronic address: chlliu@sjtu.edu.cn.

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