Ischemia detection in the context of a cardiovascular status assessment tool

Abstract

In this work a new strategy for ischemic episodes automatic detection is proposed, considering ST segment deviation and T wave and QRS morphology characteristics. A new measure of ST deviation based on time-frequency analysis, and the use of the expansion in Hermite functions technique for T wave and QRS complex morphology characterization, are the key points of the proposed methodology. HeartCycle is a European project that aims to improve life quality of coronary artery disease (CAD) and heart failure (HF) patients. Within this project, the Medical Risk Assessment module is responsible for develop models to assess cardiovascular (CV) risk and status of referred patients. The present work was performed under the context of CV status models, where myocardial ischemia plays a central role. For algorithms validation purposes, the European Society of Cardiology (ESC) ST-T database was used. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.