Abstract
Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.
Highlights
Cardiovascular disease (CVD) is the leading cause of death in many developed countries [1] and accounts for an estimated 31% of all global deaths according to the World Health Organization, making it the primary cause of mortality worldwide
RR intervals were collected from the PhysioBank Automated Teller Machine, a web service that contains waveforms annotated by cardiologists, for the data used in this study
Maximum prediction accuracies of 88.18% and 88.64% were obtained with data lengths of 10 and 20 s, respectively, at a forecast time of 0 s, whereas a minimum prediction accuracy of 64.36% was obtained with a data length of 70 s at a forecast time of 80 s
Summary
Cardiovascular disease (CVD) is the leading cause of death in many developed countries [1] and accounts for an estimated 31% of all global deaths according to the World Health Organization, making it the primary cause of mortality worldwide. CVD includes abnormal heart rhythms, called ventricular tachyarrhythmias (VTAs), which comprise ventricular fibrillation (VF) and ventricular tachycardia (VT). VTA is one of the factors causing a fast heart rate and mortality in the absence of immediate medical treatment [2]. Early prediction of VTA is important to save patients from sudden death. Heart rate variability (HRV) indices have been used as noninvasive biomarkers to predict life-threatening VTAs such as VF [3]. These indices provide features useful for distinguishing arrhythmia from normal HRV [4]. HRV signifies the time variation of the R-peaks between two successive QRS complexes. Previous studies have analyzed HRV by quantifying its features using three analysis methods: time-domain, frequencydomain, and Poincare nonlinear analyses [2, 6, 7]
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