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Feature extraction from ECG for classification by artificial neural networks

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2 Author(s)
Pretorius, L.C. ; Pretoria Univ., South Africa ; Nel, C.

The ability of properly trained artificial neural networks to correctly classify patterns makes them particularly suitable for the interpretation of ECG (electrocardiography) signals. Attention was given to three classes of ECGs, namely, normal and two cardiac myopathies, and anterior and inferior infarctions. Suitable features were extracted from the digitized bipolar limb lead ECG signals, and results are presented to show that a multilayer perceptron can correctly discriminate between the three chosen classes

Published in:

Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on

Date of Conference:

14-17 Jun 1992