Detection of Ectopic Beats in the Electrocardiogram Using an Auto-Associative Neural Network

Authors:
Lionel Tarassenko;Gari Clifford;Neil Townsend
Affiliations:
Department of Engineering Science, Oxford University, Parks Road, Oxford, OX1 3PJ, UK. E-mail: lionel@robots.ox.ac.uk;Department of Engineering Science, Oxford University, Parks Road, Oxford, OX1 3PJ, UK. E-mail: lionel@robots.ox.ac.uk;Department of Engineering Science, Oxford University, Parks Road, Oxford, OX1 3PJ, UK. E-mail: lionel@robots.ox.ac.uk
Venue:
Neural Processing Letters
Year:
2001

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Abstract

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.