Ischemic heart disease is the single most frequent cause of death inthe world today. Electrocardiogram (ECG) exercise testing is a commontool for diagnosing this disease. With the ECG exercise test one cancompare the recording of the electrical activity in the heart duringexercise and rest. A shift between the rest and exercise recording inthe segment of the heartbeat called the ST segment is used both fordiagnosing ischemia, and as input to cardiac computation methods.
Body surface potential mappings (BSPM) are ECG recordings at a greaternumber of locations on the torso, and provides better detection andlocalisation properties than the traditional 12-lead ECG. In BSPM and ECG,noise and drift from various sources are recorded in addition to thesignal propagating from the heart. A model for this is:BSPM=signal+noise+drift.Before accurate measurements of the ST segments in a BSPMcan be made, the noise and drift in the recording must be reducedwhile keeping the signal unchanged. In this thesis, an automaticalgorithm for post processing raw BSPM data recordings was made.
The following methods havebeen developed, implemented and tested as part of the algorithm:First, noise reduction methods using frequency based filteringtechniques was implemented and tested. Second, an algorithm fordetecting the BSPM signal peaks was developed. This method was used tolocate the interesting parts of each heartbeat.Third, methods for removingthe baseline drift is discussed. Four methods were selected,implemented and evaluated against each other. A method using cubicspline interpolation as an approximation to the drift was deemedbest and used in the automatic algorithm. Even after this initialprocessing, there may be noisy or corrupted signal parts present in aBSPM. Hence a framework for removing such parts of the BSPM wasdeveloped as the fourth step of the algorithm. In the fifth step, arobust method for computing the ST segment shifts at each electrodelocation from a processedBSPM was made. Finally, a tool for visualising these shifts wascreated.
The algorithm developed in this thesis was applied to BSPM recordingsof real patients. Before processing, it was not possible to computeneither reliable nor correct ST shifts from these recordings. Afterthe automatic algorithm was applied to these recordings, all theresulting BSPMs were physically realistic, and showed signs of beingclose to the true values. The computed ST shifts from the processeddata showed promising results for diagnosing ischemia in the limitedset of BSPMs available. In addition, a comparison between traditionalECG and BSPM was made.