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.
Highlights
Ventricular tachyarrhythmia (VTA) causes a rapid heart rate and eventual death in the absence of immediate medical intervention (Lee et al, 2016)
The result shows that the QRS complex shape features extracted from the ECG could have an impact in predicting ventricular fibrillation (VF) before its occurrence in terms of its prediction performance
We found that only the QRS complex shape or that combined with heart rate variability (HRV) can improve the performance of predicting VF
Summary
Ventricular tachyarrhythmia (VTA) causes a rapid heart rate and eventual death in the absence of immediate medical intervention (Lee et al, 2016). Because measuring and analyzing electrocardiogram (ECG) signals is an efficient way to identify electrical conduction malfunctions in the heart, such as arrhythmias, Prediction of Ventricular Fibrillation previous studies have attempted to predict the occurrence of VT, VF, or both by investigating electrocardiography (ECG) (Riasi and Mohebbi, 2013; Cappiello et al, 2015; Melillo et al, 2015). Various methods have been introduced to predict VTA (VF, VT, or both), such as by assessing QRS (Q, R, and S waves in ECG) duration, T wave alternans, left ventricular impairment, QT (from the start of Q wave to the end of T wave in ECG) dispersion, and heart rate variability (HRV) (Lane et al, 2005). HRV has been analyzed to quantify its features using three analysis methods: time domain, frequency domain, and Poincare non-linear analyses (Bilgin et al, 2009; Joo et al, 2012; Lee et al, 2016)
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