A virtual reality toolkit for the diagnosis and monitoring of myocardial infarctions

Abstract

We have developed a software system that takes standard electrocardiogram (ECG) input and interprets this input along with user-defined and automatically defined markers to diagnose myocardial infarctions (MI). These pathologies are then automatically represented within a volumetric model of the heart. Over a period of six months 30 patients were monitored using a digital ECG system and this information was used to test and develop our system. It was found that the STEMIs (ST segment Elevation MI) were successfully diagnosed, however NSTEMIs (Non-STEMI), although correctly interpreted, were more ambiguous due to the fact that T wave inversions are sometimes seen on normal ECGs. Control ECGs of normal hearts were also taken. The system correctly interpreted this data as being normal. A standard voxel-count metric was developed so that future work in MI monitoring will be possible. The toolkit was found to be beneficial for three possible uses, as a diagnostic tool for clinicians, as a teaching tool for students and also as an information tool for the patient.