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A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (ECG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCU's).

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The correct classification of the beats relies heavily on the efficiency of the features extracted from the STsegment and on the desired abilities of algorithm on sensitivity and specificity indices. Nonlinear Principal Component Analysis (NLPCA) is a recently proposed method for nonlinear feature extraction. It has been observed to have better pelformance for representing complex ST segment features of normal and abnormal cases. The function of representation was created using only normal patterns from the same patient. The distribution of these patterns is modeled using a Radial Basis Function Network (RBFN). This model is capable of finding abnormal patterns with high sensitivity while the specificity is also acceptable (> 70%), and we can acomplish correct classification rates of higher than 90% for the ischemic beats in many files of the European ST-T database. This technique may be used, in general, for other classification problems in medicine and other disciplines.