Abstract Coronary congestion is a heart disease that puts many lives at risk each year. The task of coronary arteries is to distribute blood to the heart tissue and any blockage in them can cause the tissue to absorb less oxygen and nutrients than needed (ischaemia disease). This imbalance will continue until the first cell is destroyed (myocardial infarction). Simulating the myocardial infarction in the laboratory rats, this study tries to determine the extent of tissue damage through the electrocardiogram (ECG) and atrial blood pressure (ABP) synchronic signals. The signals of 50 wistar rats with a weight range of 200-300 g were recorded at 30 min in the normal case and 30 min in the ischaemia and myocardial infarction (MI) case (the artificial complete blockage was in the left anterior descending coronary artery (LAD)). For a different injury in the rats' heart, the vasopressin (AVP) with different doses was injected to 40 rats. After that the images of the heart sections and the data were extracted, the 50-dimensional feature vector was generated by using the wavelet packet transform (WPT) on the ECG and ABP signals and also by obtaining the entropy of the wavelet coefficients. The extent of tissue damage on the images of the heart tissue was extracted by using the image processing method. Finally, the amount of the damaged tissue was estimated by four artificial neural networks (ANN) (with different structures) with an averaging criterion. The intelligent machine estimated the ischaemia and normal tissues with the average error of 2.91% for all the AVP doses and control cases.