A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation.

Abstract

Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.