A supervised neural network (NN) 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 ischemic episodes detection. Aggregate statistics for the description of the detector performance were used due to the small number of events. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm reduces dramatically training time (10-fold decrease in our case) when compared to the classical BP algorithm. The resulting NN is capable of detecting ischemia independently of the lead used. It was found that the average ischemia episode sensitivity is 88.62% while the average ischemia sensitivity is 72.22%. This drop in ischemia sensitivity could be attributed to the diverse statistical properties of the ECGs within the same patient. The results show that NN can be used in ECG processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCUs).
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