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Title of Journal: Ann Biomed Eng

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Abbravation: Annals of Biomedical Engineering

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Springer US

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DOI

10.1002/app.35060

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1573-9686

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A Statistically Based Acute Ischemia Detection Alg

Authors: Bruce Hopenfeld M Sasha John Tim A Fischell Steven R Johnson
Publish Date: 2012/06/28
Volume: 40, Issue: 12, Pages: 2627-2638
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Abstract

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 startup phase the algorithm processes electrocardiogram signals to determine a normal range of STsegment deviation as a function of heart rate The algorithm then generates upper and lower STdeviation thresholds based on the dispersion of the baseline STdeviation data After the startup phase persistent STdeviation that is beyond either the upper or lower thresholds results in detection of acute ischemia To test the algorithm we performed longterm 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 10day 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 subjectspecific heartrate dependent STdeviation thresholds can detect PCI induced myocardial ischemia without resulting in false positive detections in a small control groupThis work was funded in part by SBIR grant 1R43HL09615801 from the National Heart Lung and Blood Institute of the National Institutes of Health The contents of this work are solely the responsibility of the authors and do not necessarily reflect the official views of the NIH or the NHLBI The authors would like to thank Renee Neuharth RN and Sandy Wilson RN the study coordinators at the University of Utah and Borgess Medical Center respectively The authors would also like to thank Milan Horácek for providing the bodysurface datasets that were used to help select the bipolar leads assessed in this study Finally the authors would like to thank Rob Granger at Angel Medical Systems for his help in preparing the figures in this paper

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