A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression.

Journal: Bio-medical materials and engineering
PMID: 26405919

Abstract

Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

Authors

  • Jianli Yang
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
  • Yang Bai
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
  • Guojun Li
    Department of Urology, Xiangya Changde Hospital, 415000 Changde, Hunan, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Xiuling Liu
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.

Keywords

External Resources

Popular Topics
Recent Journals