AN APPROACH TO INTELLIGENT ISCHEMIA MONITORING

The paper describes an approach to intelligent ischaemia event detecti on based on ECG ST-T segment analysis. ST-T trends are processed by me ans of a Bayesian forecasting approach using the multistate Kalman fil ter. A complete procedure, intended for use in CCU/ICU monitoring area s, is proposed, in order to give the clinician an intelligent monitori ng tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain featur es of ST-T elevation/depression not detected by other means, and to re ject artefacts and erroneous events. A sensivity of 89.58% and a predi ctivity of 84.31% are obtained on selected records of the European ST- T database. Using a restriction on event amplitude, the predictivity i s raised to 95.55%. An ischaemia sensitivity index of 1.2 was determin ed. The method has been shown to be a robust and practical trend analy sis tool, and seems to be appropriate for numeric/symbolic transformat ions in next-generation intelligent monitoring systems.