A remote healthcare monitoring system for faster identification of cardiac abnormalities from compressed ECG using advanced data mining approach

Abstract

Cardiac Disease has become very common perhaps because of increasingly busy lifestyles. The rapid advancement of mobile communication technologies offers innumerable opportunities for the development of software and hardware applications for remote monitoring of chronic disease. This paper describes a remote health-monitoring service that provides an end-to-end solution. We present an efficient data mining-based solution that recognizes different CVDs (such as ventricular flutter/fibrillation, atrial fibrillation, atrial premature beat, premature ventricular contraction) from the compressed ECG, it was proposed to perform real-time classification of Cardiac Vascular Disease (CVD) based on data mining techniques. The subset of the features selection from the compressed ECG was performed using the Genetic algorithm and the clustering was performed using Expectation Maximization. © 2013 Springer.