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Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks

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Artificial Neural Networks in Medicine and Biology

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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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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References

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Author information

Authors and Affiliations

  1. Department of Theoretical Physics, Lund University, Lund, Sweden

    Mattias Ohlsson

  2. Department of Clinical Physiology, Lund University, Lund, Sweden

    Holger Holst & Lars Edenbrandt

Authors
  1. Mattias Ohlsson
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  2. Holger Holst
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  3. Lars Edenbrandt
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Editor information

Editors and Affiliations

  1. Department of Philosophy, Göteborg University, Box 200, SE-405 30, Göteborg, Sweden

    Helge Malmgren BA, PhD, MD

  2. Department of Electrical Engineering, Linköping University, SE-585 83, Linköping, Sweden

    Magnus Borga MSc, PhD

  3. Department of Computer Science, University of Skövde, PO Box 408, SE-541 28, Skövde, Sweden

    Lars Niklasson BSc, MSc, PhD

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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

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