Ventricular Tachyarrhythmia Prediction based on Heart Rate Variability and Genetic Algorithm

Abstract

Abstract

Ventricular Tachyarrhythmias Prediction Methods and its Prognostic Features: A Review

“…Thus, optimising the SVM based classifier's performance. In this study, the proposed method achieved 79.41% accuracy, 77.94% sensitivity and 80.88% specificity [118]. However, this study is yet limited by the small sample size used in predicting the VTAs event although it used the highest number of sample size compared to the method aforementioned.…”

“…Boon et al [118] proposed an improved VTAs prediction method based on HRV and Support Vector Machine (SVM) classifier. The dataset used was the Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0) obtained from the PhysioNet [114].…”

A Designing Dairy Cattle Facilities Based on Statics and Dynamic Zoometry by Using Artificial Neural Network

“…Genetic algorithms have to solve this problem because this method has been used extensively to find the optimum conditions for example in studies following, algorithm genetics is used to predict the onset of tachyarrhythmias ventricle provides an opportunity to reduce the loss due to sudden cardiac death [13], genetic algorithms integrate thinking immunity the biology of the immune system [14], and in the determination of optimal parameter through simulation [15]. The BPNN will train the database with genetic algorithm (GA) optimisation in 2 hidden layers.…”