Prediction Algorithm of Malignant Ventricular Arrhythmia Validated across Multiple Online Public Databases

Abstract

Prediction of malignant ventricular arrhythmia (mVA) is essential to prevent sudden cardiac death. There were mainly three research clusters on mVA prediction using electrocardiogram (ECG): prediction using CUDB, SDDB and private databases. Comparability and generalization issue arose due to the different usage of arrhythmic datasets for analysis. Very few studies attempted short-term prediction of mVA using multiple databases, and those studies achieved low prediction performance. Our study aims to improve the prediction performance involving multiple databases and to promote the algorithm comparability by performing more comprehensive comparability study while including a more complete set of data available from the public databases. In our study, eight statistical box count features derived from phase space reconstruction on ECG signal were classified using maximum thresholding method. This was followed by performance benchmarking against the first two clusters of existing research and a performance evaluation using the combined set of databases. Our algorithm using box count coefficient of mean absolute deviation achieved over 90% of accuracy and over 4-minutes prediction time for all the three set of performance evaluations. This algorithm outperforms the existing work by introducing lower computational efforts.