Modeling ECG signals with regard to the location and intensity of myocardial infarction

In this paper we used neural network (NN) to generate ECG signals with regard to the location and intensity of myocardial infarction (MI) as input of the model. We can use this model in educational programs and assessment of diagnostic devices. We can also use the model in telemedicine applications. We used 50 samples of labeled ECG and used 70% of them for training and 30% for test. Addressing of MI location is the standard 17-segments for left ventricle. The measure of Mi intensity was the normalized under curve area of ECG in one cycle. For creating the proper shapes of ECG we used NN and for repeating the ECG cycles we used an Integral Pulse Frequency Modulator (IPFM) with a fixed threshold. However it is possible to use any Heart Rate Variability (HRV) model. We used two kind of NN. One was multi layer perceptron (MLP) with one hidden layer and the second was radial basis function (RBF) NN and compared the results. After evaluating both NN we realized that the performance of both were more or less the same. The result of evaluation of the model satisfied cardiologist. A new model for generating ECG signals related to the location and intensity of MI was presented.