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BioMed Research International
Volume 2018, Article ID 1315357, 14 pages
https://doi.org/10.1155/2018/1315357
Research Article

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

College of Information and Computer, Taiyuan University of Technology, Taiyuan, China

Correspondence should be addressed to Dengao Li; nc.ude.tuyt@oagnedil

Received 27 February 2018; Accepted 28 May 2018; Published 3 July 2018

Academic Editor: Yudong Cai

Copyright © 2018 Dengao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

J wave is the bulge generated in the descending slope of the terminal portion of the QRS complex in the electrocardiogram. The presence of J wave may lead to sudden death. However, the diagnosis of J wave variation only depends on doctor’s clinical experiences at present and missed diagnosis is easy to occur. In this paper, a new method is proposed to realize the automatic detection of J wave. First, the synchrosqueezed wavelet transform is used to obtain the precise time-frequency information of the ECG. Then, the inverse transformation of SST is computed to get the intrinsic mode function of the ECG. At last, the time-frequency features and SST-based and the entropy features based on modes are fed to Random forest to realize the automatic detection of J wave. As the experimental results shown, the proposed method has achieved the highest accuracy, sensitivity, and specificity compared with existing techniques.