Estimation of extent damage tissue by multi resolution analysis of the electrocardiogram and arterial blood pressure

Introduction: Coronary artery congestion is a heart disease which causes a lack of oxygen and nutrients in the heart, and is felt as chest pain (ischemia disease). Prolonged ischemia can continue until the cells start to dye, which is called myocardial infraction. Aims: We aim to determine the amount of cardiac tissue damage by multi resolution analysis of electrocardiogram (ECG) and atrial blood pressure (ABP) signals. Methods: In this study 39 Wistar rats were used, and ECG and ABP signals were recorded to estimate the extent of tissue damage. The signals were recorded for 30 minutes during normal heart function and for 30 minutes during ischemia and myocardial infraction (MI) which was induced by artificial complete blockage of the left anterior descending coronary artery (LAD). Additionally the vasopressin (AVP) in various doses was injected to 39 rats. Afterwards, the wavelet packet transform (WPT) wad applied to the recorded ECG and ABP signals for decomposition into dyadic scales. 50 dyadic scales which were more informative for discrimination of different ischemic phases among each other and from the healthy phase were chosen, and a feature vector as the entropy of corresponding wavelet coefficients was selected. As a reference measurement of the extent of damage tissue, images of the heart sections were additionally extracted, and the extent of damage tissue was assessed by image processing technique. Finally, the amount of damage tissue was classified with artificial neural networks (ANN) based techniques. Results: The extent of tissue damage was estimated based on the ANN and multi-resolution analysis of the synchronic electromechanical signals with the average error of the 2.17% for the normal and ischemic tissue in all the AVP doses.