Abstract
This paper presents a neural network classifier for the diagnosis of acute myocardial infarction, using the 12-lead ECG. Features from the ECGs were extracted using principal component analysis, which allows for a small number of effective indicators. A total of 4724 pairs of ECGs, recorded at the emergency department, was used in this study. It was found (empirically) that a previous ECG, recorded on the same patient, has a small positive effect on the performance for the neural network classifier.
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© 2000 Springer-Verlag London
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Ohlsson, M., Holst, H., Edenbrandt, L. (2000). Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_31
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_31
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
eBook Packages: Springer Book Archive