Detection of ectopic beats in the electrocardiogram using an auto-associative neural network

Abnormal rhythms of the heart are often preceded by the occurrence of ectopic beats. These are difficult to detect as their shape is not very different from that of a normal QRS complex, the main feature in the electrocardiogram. We show how an auto-asociative multi-layer perceptron can be trained to detect normal beats only, so that the subtle abnormalities in shape of ectopic beats become clearly identifiable. This is a generic detector of abnormal beats (i.e. beats whose morphology is different from that of a normal beat) and we use ventricular ectopic beats to illustrate the performance of the algorithm. We also propose a new parameter, the variance ratio, to monitor the progress of learning in an auto-associative network.