Knowledge based signal processing and scoring system for the exercise electrocardiogram

Analysis of exercise electrocardiogram (ECG) signals is used to evaluate the status of patients suspected of having ischemia problems. Since exercise tests generate a large volume of ECG signals, several computerized systems have been built to match the special needs of the exercise testing using predesigned methods. If systems, however, have all been programmed with fixed processing algorithms set by the developer, there is limited ability for the user to modify the methodologies used to obtain measurements and to interpret test results. The goal of this research was to develop a knowledge base controlled digital signal processing and numerical scoring system. The system's flexibility was provided by the knowledge based structure other than predesigned subroutines. The scores are numerical results calculated by user defined scoring equations and rules based on the test data. Each test can have one or more scores which are the integration of the measurements and can be treated as the medical decision of the test. With the knowledge based structure, the system can be used not only as a clinical tool for evaluation of coronary artery disease using accepted predefined scoring rules, but also as an exercise testing methodology research tool to test user defined rules. Results accuracy and the system flexibility have been tested based on real data from patients and artificial data. The system is now being used at the exercise testing facility of LDS Hospital, Salt Lake City, Uta