Earlier Prediction Algorithm of Malignant Ventricular Arrhythmia on Heterogenous Databases: A Review

Prediction of malignant ventricular arrhythmia (mVA) is utmost imperative to enable earlier medical intervention and prevent sudden cardiac death (SCD). However, patients with a history of coronary artery disease (CAD) and congestive heart failure (CHF) are at higher risk of SCD. Some background knowledge on SCD and cardiovascular diseases (CVD) associated with it, including mVA, CAD and CHF are first introduced. Then, the previous works on prediction of mVA, CAD and CHF, as well as early prediction time of these diseases are reviewed and discussed. The prediction algorithm research were separated into five subtopics: prediction of Malignant Ventricular Arrhythmia (mVA), prediction of mVA using Multi Class Approach, detection of CAD, detection of CHF, prediction of mVA’s earlier prediction time. From here, the research gap was identified; the feature selection is utmost significant to allow exploration and investigation on the statistical relationship of different cardiac diseases to the development of mVA events over time, investigation of mVA prediction in normal patient, patients with CAD and also CHF, by using heterogenous databases are lack of research, an algorithm with an earlier prediction time and an adequately high performance could be further explored so that imminent mVA could be predicted with a minimum chance of error and an optimum duration for early diagnosis, HRV technique shall be employed by using longer signal and then evaluating the algorithm using overall performance metrics. The review process was done by using several appropriate keywords as below through google engine. Finally, the previous research works are summarized in terms of their limitations.