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Differentiation Between Normal and Epileptic EEG Using K-Nearest-Neighbors Technique

  • Jefferson Tales Oliva
  • João Luís Garcia Rosa
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)

Abstract

Epilepsy is one of the most common neurological disorder. This disorder can be diagnosed by non-invasive examinations, such as electroencephalography, whose records are called electroencephalograms (EEG). The EEG can be stored in medical databases for reusing in future. In these data, one can apply data mining process supported by machine learning techniques in order to find patterns that can be used for building predictive models. This paper presents an application of the cross-correlation technique and the kNN algorithm for classification in a set with 200 EEG segments in order to differentiate normal and epileptic (abnormal) signals. The results were evaluated using 10-fold cross-validation and contingency table methods. With the evaluation using cross validation, it was not found statistically significant difference for classification using kNN. The contingency table results found that the kNN with k = 1 and k = 7 performed better for classifying abnormal and normal EEG, respectively. Also, the kNN with k = 1 and k = 7 were more likely to correctly classify normal and abnormal EEG, respectively.

Keywords

Feature Extraction Motor Imagery Common Neurological Disorder Motor Imagery Task Weka Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

J.T. Oliva would like to thank the Brazilian funding agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support. J.L.G. Rosa is grateful to the Brazilian agency FAPESP (process 2016/02555-8) for the financial support.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jefferson Tales Oliva
    • 1
  • João Luís Garcia Rosa
    • 1
  1. 1.Bioinspired Computing Laboratory, Institute of Mathematics and Computer ScienceUniversity of São PauloSão CarlosBrazil

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