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.
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Tarassenko, L., Clifford, G. & Townsend, N. Detection of Ectopic Beats in the Electrocardiogram Using an Auto-Associative Neural Network. Neural Processing Letters 14, 15–25 (2001). https://doi.org/10.1023/A:1011373923479
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DOI: https://doi.org/10.1023/A:1011373923479