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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Physiol. | doi: 10.3389/fphys.2019.01193

Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape

  • 1Kumoh National Institute of Technology, South Korea
  • 2Department of Mechanical and Biomedical Engineering, Kangwon National University, South Korea

Early prediction of the occurrence of ventricular tachyarrhythmia (VTA) has a potential to save patients’ lives. VTA includes ventricular tachycardia (VT) and ventricular fibrillation (VF). Several studies have achieved promising performances in predicting VT and VF using traditional heart rate variability (HRV) features. However, as VTA is a life-threatening heart condition, its prediction performance requires further improvement. To improve the performance of predicting VF, we used the QRS complex shape features, and traditional HRV features were also used for comparison. We extracted features from 120-s-long HRV and electrocardiogram (ECG) signals (QRS complex signed area and R-peak amplitude) to predict the VF onset 30 s before its occurrence. Two artificial neural network (ANN) classifiers were trained and tested with two feature sets derived from HRV and the QRS complex shape based on a 10-fold cross-validation. The prediction accuracy estimated using 11 HRV features was 72%, while that estimated using four QRS complex shape features yielded a high prediction accuracy of 98.6%. The QRS complex shape could play a significant role in performance improvement of predicting the occurrence of VF. Thus, the results of our study can be considered by the researchers who are developing an application for an implantable cardiac defibrillator (ICD) when to begin ventricular defibrillation.

Keywords: QRS complex shape, QRS complex singed area, R-peak amplitude, Ventricular Fibrillation, machine learning

Received: 11 Mar 2019; Accepted: 03 Sep 2019.

Copyright: © 2019 Taye, Shim, Hwang and Lim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Han-Jeong Hwang, Kumoh National Institute of Technology, Gumi, North Gyeongsang, South Korea, h2j@kumoh.ac.kr
Prof. Ki Moo Lim, Kumoh National Institute of Technology, Gumi, North Gyeongsang, South Korea, kmlim@kumoh.ac.kr