Abstract
Using the European Society of Cardiology ST-T Database, we have developed a Karhunen–Loève transform-based algorithm for robust automated detection of transient ST segment episodes during ambulatory ECG monitoring. We review current approaches and systems to detect transient ST segment changes and describe the architecture of our algorithm and its development. The algorithm incorporates a single-scan trajectory-recognition technique in feature space using the Mahalanobis distance function between the feature vectors. The main characteristics of the algorithm are detection of noisy beats, correction of the reference ST segment level to correct for slow ST level drift, detection of sudden significant shifts of ST deviation due to shifts of the mean electrical axis of the heart, detection of transient ST episodes, and, by tracking the QRS complex morphology, differentiation between ischemic and nonischemic ST episodes as a result of axis shifts. We compared the algorithm's performance to other recently developed algorithms and estimated its real-world performance.
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