Real-Time Detection of Myocardial Infarction by Evaluation of ST-Segment in Digital ECG

Abstract

Myocardial infarction (MI) is one of the most common sudden-onset heart diseases. Early diagnosis and man-agement of heart ischemia result in good prognosis. Early changes in the heart muscle activity after ischemiareflect in ST segment elevation on electrocardiogram (ECG) recordings. With the development of signal process-ing techniques and the portable devices, there is a need to develop a real-time algorithm that accurately detectsMI non-invasively. In this paper, we propose a computer algorithm that employs digital analysis scheme towardsthe real-time detection of MI. The proposed algorithm extract features based on clinical diagnosis conditionsallowing the continuous analysis of ST segment and simultaneous detection of abnormal heart activity resultingfrom MI. Using an online ECG library of patient data, the signals were filtered for high frequency noise, baselinedrift then features of interest (Q, R, S waves and J points) were extracted. These were used to measure the STsegment elevation and depression as an important indicator of MI defined in clinical guideline for MI diagnosis.The developed algorithm was capable of detecting MI with 85% sensitivity and 100% specificity in a test set of40 ECG recordings.