Analytical Progression Scale for Arrhythmia Scope prediction from Electrocardiograms - Jyothi, Sreedhar; Nelluri, Geethanjali (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Machine Learning (ML) techniques have exploded in popularity, especially the use of ML in automated ECG interpretation, which has been widely addressed in the literature. Other applications of machine learning in cardiac electrophysiology as well as arrhythmia are even less well recognised. Yet, the contemporary models are evincing the considerable false alarming in the process of arrhythmia prediction. In order to improve the arrhythmia prediction accuracy, this manuscript portrayed a novel analytical progression scale (APS) that learns from the given input electrocardiograms with appropriate label positive (prone to arrhythmia) or negative (not prone to arrhythmia). The experimental study has carried a 10-fold cross validation strategy on proposed and other contemporary models to scale the performance advantage of the proposed Analytical Progression Scale that compare those statistical values obtained for performance metrics such as precision, sensitivity, specificity, and accuracy. The results obtained from cross validation are evincing that the proposed model APS is outperforming the other contemporary models.