Heart ischemia, the precursor to an infarction, is one of the most common diseases in the western world. Today, the electrocardiogram or ECG is the most widely used tool to diagnose the disease. However, it often fails to detect the ischemia or to give an adequate picture of the size and location.
Therefore, the potential of increasing knowledge obtained through mathe- matical models is very high. In this thesis the bidomain model is used to describe the electrical activity in the heart and body with ischemia incorporated into the model. To solve the equations set up by the bidomain model, the finite element method is used. Different physiological variations have been made to the body, these include changing the location of the heart and varying the conductivities in the body. The solution to the equations is then studied at the body surface. The main question asked is whether it is possible to detect the location and size of different types of ischemia by analyzing the solution.
The methods used for this have been Singular Value Decomposition and Su- pervised learning. The different vectors obtained from the decomposition are used to distinguish the location and size of the ischemia for different physiolog- ical variations.
The results show that it is possible to distinguish the location of the ischemia but that it probably will be more difficult to find the correct size since the change in size is harder to separate from other physiological variations, such as the conductivity of the body.
Although relatively simple methods have been used, they indicate that, with further development, they can be used for the purpose of detecting the different ischemia.
2006. , 43 p.