A Statistically Based Acute Ischemia Detection Algorithm Suitable for an Implantable Device

This study investigates the performance of a new statistically driven acute ischemia detection algorithm that can process data from two bipolar cutaneous or subcutaneous leads. During a start-up phase, the algorithm processes electrocardiogram signals to determine a normal range of ST-segment deviation as a function of heart rate. The algorithm then generates upper and lower ST-deviation thresholds based on the dispersion of the baseline ST-deviation data. After the start-up phase, persistent ST-deviation that is beyond either the upper or lower thresholds results in detection of acute ischemia. To test the algorithm, we performed long-term (10 day) Holter monitoring in a control group of 14 subjects. We also performed Holter monitoring during balloon angioplasty, and for 2 days after surgery, in 30 subjects who underwent elective percutaneous coronary interventions (“PCI”). We determined the percentage of balloon inflations the algorithm detected without producing false positive detections within the control group 10-day daily life data. The algorithm detected 17/17 LAD occlusions, 7/8 LCX occlusions, and 8/9 RCA occlusions. Our results suggest that automatically generated, subject-specific, heart-rate dependent ST-deviation thresholds can detect PCI induced myocardial ischemia without resulting in false positive detections in a small control group.