ArticleFrontiers in physiology2019
Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.
Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.EBioMedicine · 2023Pooled it
- Premature ventricular contraction-mediated ventricular fibrillation: Clinical characteristics, application of machine-learning algorithm, and outcomes of catheter ablation-A case series.Heart rhythm O2 · 2026Article
- Understanding the shortcomings of heart rate variability as a tool for autonomic analysis.Frontiers in physiology · 2026Article
- Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.Diagnostics (Basel, Switzerland) · 2022Review
- A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks.Chaos (Woodbury, N.Y.) · 2022Article
- Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques.Journal of personalized medicine · 2022Article
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
- Comparison of Mobile Application-Based ECG Consultation by Collective Intelligence and ECG Interpretation by Conventional System in a Tertiary-Level Hospital.Korean circulation journal · 2021Article
- Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems.Computational intelligence and neuroscience · 2021Article
- Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies.Frontiers in physiology · 2021Review
- Trends in Heart-Rate Variability Signal Analysis.Frontiers in digital health · 2021Review
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4 authors.
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Abstract
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.
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