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Predictive Models for Differentiation Between Normal and Abnormal EEG Through Cross-Correlation and Machine Learning Techniques

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Book cover Towards Integrative Machine Learning and Knowledge Extraction

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

Currently, in hospitals and medical clinics, large amounts of data are becoming increasingly registered, which usually are derived from clinical examinations and procedures. An example of stored data is the electroencephalogram (EEG), which is of high importance to the various diseases that affect the brain. These data are stored to keep the patient’s clinical history and to help medical experts in performing future procedures, such as pattern discovery from specific diseases. However, the increase in medical data storage makes unfeasible their manual analysis. Also, the EEG can contain patterns difficult to be observed by naked eye. In this work, a cross-correlation technique was applied for feature extraction of a set of 200 EEG segments. Afterwards, predictive models were built using machine learning algorithms such as J48, 1NN, and BP-MLP (backpropagation based on multilayer perceptron), that implement decision tree, nearest neighbor, and artificial neural network, respectively. The models were evaluated using 10-fold cross-validation and contingency table methods. The evaluation results showed that the model built with the J48 performed better and was more likely to correctly classify EEG segments in this study than 1NN and BP-MLP, corresponding to 98.50% accuracy.

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Notes

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Acknowledgment

I would like to thank the Brazilian funding agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support.

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Authors and Affiliations

  1. Bioinspired Computing Laboratory, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, 13566–590, Brazil

    Jefferson Tales Oliva & João Luís Garcia Rosa

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  1. Jefferson Tales Oliva
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  2. João Luís Garcia Rosa
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Correspondence to Jefferson Tales Oliva .

Editor information

Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Oliva, J.T., Garcia Rosa, J.L. (2017). Predictive Models for Differentiation Between Normal and Abnormal EEG Through Cross-Correlation and Machine Learning Techniques. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69774-1

  • Online ISBN: 978-3-319-69775-8

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