The analysis of the electrocardiogram as a diagnostic tool is a relatively old field and it is therefore often assumed that the ECG is a simple signal that has been fully explored. However, there remain difficult problems in this field that are being incrementally solved with advances in techniques from the fields of filtering, pattern recognition, and classification, together with the leaps in computational power and memory capacity that have occurred over the last couple of decades. While the ECG is routinely used to diagnose arrhythmias, it reflects an integrated signal and cannot provide information on the micro-spatial scales of cells and ionic channels. For this reason, the field of computational cardiac modeling and simulation has grown over the last decade. The aim of this paper is by using a novel system to develop methods to analyze and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorder
