Ventricular Tachyarrhythmia Onset Prediction Based on HRV and Genetic Algorithm

Abstract: Predicting onset of ventricular tachyarrhythmia provides opportunities to reduce casualties due to sudden cardiac death. However, the prediction accuracy still needs improvement. Therefore, we aim to propose a method that can predict the onset of tachyarrhythmia events with improved accuracy based on heart rate variability and Support Vector Machine classifier. Fifty percent of sample data from standard database was used to train the classifier, and the remainder was used to verify the performance. Five minutes RR intervals immediately prior to tachyarrhythmia event from each sample data was cropped for ectopic beat correction and then converted to heart rate. Extraction of time domain, spectral, non-linear and bispectrum features were performed subsequently. Furthermore, genetic algorithm was used tosimultaneously optimize the feature subset and classifier parameters. With the optimization, prediction accuracy of our proposed method able to outperform previous works with 77.94%, 80.88% and 79.41 % for sensitivity, specificity and accuracy respectively.
Keywords: Heart Rate Variability, Arrhythmia Prediction, Ventricular Tachyarrhythmia (VTA), Genetic Algorithm, Bispectrum features
Author: K. H. Boon
Journal Code: jptkomputergg160280

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