Application of AI in Cardiology

This work is deals with processing and analysis of ECG waves, namely with recognition of ECG samples with diagnosis of myocardial infarct and arrhythmia from samples. As a base concept for comparing the ECG wave to the typical wave,Template matching method is used, which can find the best similarity between the test sample and ECG templates. With respect to the metrics it calculates their relative similarity, too. Input data were obtained from the project PhysioNet, gathered at the Institute of Cardiology at the University Clinic Benjamin Franklin in Berlin and digitalized in the National Metrology Institute, Germany under the name PTB ECG database. The outputs are the similarity coefficients of the twelve conventional ECG leads and the six basic parameters of waves. The results of our proposal with used methods for data preprocessing and implemented algorithm are comparable with the results obtained by systems based on neural networks classification. It has the potential to help physicians in the initial analysis and identification of the patient´s condition.