Neural network for automatic anomalous QRS complex detection

An application of the back-propagation (BP) neural network (NN) for the discrimination between normal and pathological electrocardiogram (ECG) complexes is presented. The BP is used as a part of an unsupervised method: the network output has not been used to discriminate normal and pathological complexes, but only to extract the prototype complex of the analyzed ECG. An attempt is made to automatically individualize a pathological QRS morphology on those ECGs where anomalous premature ventricular contraction (PVC) beats were less than 15%. Results show a sensitivity of 0.991 and a specificity of 0.985.<
>