Ventricular tachyarrhythmias prediction methods and its prognostic features: a review

Abstract

This paper presents a literature review of ventricular tachyarrhythmias (VTAs) prediction methods and its prognostic features, as well as highlights the severity of the cardiovascular diseases in general population. This article provides the collective review of the short-term VTAs prediction based on the machine learning methods associated with the potential prognostics electrocardiogram (ECG) characteristics features that have been proposed in the recent literature. The basic morphology of the ECG waveform and its working principle is also briefly described for better understanding of the relationship between the ECG characteristics features and the occurrence of VTAs. In addition, the trend and future direction in the development of VTAs prediction system with machine learning are presented as well. It is desired that the progressive development of real-time, low computational cost and reliable short-term VTAs prediction algorithm in coming years could decrease the mortality rate of cardiovascular diseases within general populations. This article can be adopted as an initial idea and guidelines for beginners in this field to initiate their research.