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Fusion of Signal and Differential Signal Domain Features for Epilepsy Identification in Electroencephalogram Signals

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Advances in Data and Information Sciences

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 38))

Abstract

Epilepsy is a common neurological disorder and the number of epilepsy patients around the world is increasing in an alarming rate. Identifying and controlling epilepsy is a challenging task. Traditionally, electroencephalogram (EEG) is the most dependable method for the rigorous understanding of epilepsy states. In this paper, a fusion of signal and differential domain features are presented for the effective analysis and identification of epileptic EEG signals. The results of the proposed method for the identification of epilepsy in EEG signal are promising.

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Acknowledgements

The authors would like to thank Central University of Kerala for providing research and financial support. The authors would also like to thank the reviewers for their valuable comments to improve the quality of paper.

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Authors and Affiliations

  1. Department of Computer Science, Central University of Kerala, Periye, Kasaragod, Kerala, India

    O. K. Fasil, R. Rajesh & T. M. Thasleema

Authors
  1. O. K. Fasil
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  2. R. Rajesh
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  3. T. M. Thasleema
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Corresponding author

Correspondence to O. K. Fasil .

Editor information

Editors and Affiliations

  1. Smart Grid and Renewable Energy, University of Agder, Kristiansand, Norway

    Mohan L. Kolhe

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Department of Computer Science and Engineering, The Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India

    Vikash Kumar Singh

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Fasil, O.K., Rajesh, R., Thasleema, T.M. (2018). Fusion of Signal and Differential Signal Domain Features for Epilepsy Identification in Electroencephalogram Signals. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 38. Springer, Singapore. https://doi.org/10.1007/978-981-10-8360-0_12

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  • DOI: https://doi.org/10.1007/978-981-10-8360-0_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8359-4

  • Online ISBN: 978-981-10-8360-0

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