The development of electrocardiograph, ECG, software is proposed to improve the accuracy of the detection of acute myocardial infarcts, AMIs. This effort is based on the pioneering work of Dr. R. Selvester and J. Solomon in the field of computerized electrocardiography. Using an advanced electrophysiological computer model of the heart, we propose to create a rich and robust database of synthetic ECG signals. A combination of these synthetic ECGs and ECGs from a learning subset of the Long Beach Memorial ECG database will be used to develop rules and criteria for use in an interpretive ECG algorithm. The resulting interpretive algorithm will be tested using a large test subset chosen from the Memorial database. We propose to develop interpretive software that will significantly improve the detection, location, and sizing of acute myocardial infarcts over that of commercial interpretive ECG algorithms. We expect that this effort will lead to significant improvement in patient care and reduced healthcare costs associated with heart attacks.
The ECG is the first test that is performed on a patient with chest pain. An improvement in ECG software to detect an acute MI would significantly reduce the time to therapy and increase the chance of limiting or reversing cardiac damage. The market for ECGs based equipment with accurate MI detection capability is over $300 million dollars annually. The proposed software could be used in electrocardiographs, defibriliators, and acute care monitoring equipment.
