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Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal

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Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1042))

Abstract

Epilepsy is a neurological affliction that in impact around 1% of humankind. Around 10% of the United States populace involvement with minimum a solitary convulsion in their activity. Epilepsy has recognized respectively tendency of the cerebrum outcomes unforeseen blasts of weird electrical action which disturbs the typical working of the mind. Since spasms by and large happen once in a while and are unforeseeable, seizure identification frameworks are proposed for seizure discovery amid long haul electroencephalography (EEG). In this exploration, we utilize DWT for highlight extraction and do correlation for all kind of Machine learning order like SVM, Nearest Neighbor Classifiers, Logistic relapse, Ensemble classifiers and so on. In this examination classification accuracy of Fine Gaussian SVM recorded as 100% and it has better as compare to other existing machine learning approaches.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology, Raipur, India

    Rekh Ram Janghel, Archana Verma & Yogesh Kumar Rathore

Authors
  1. Rekh Ram Janghel
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  2. Archana Verma
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  3. Yogesh Kumar Rathore
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Corresponding author

Correspondence to Rekh Ram Janghel .

Editor information

Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Janghel, R.R., Verma, A., Rathore, Y.K. (2020). Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_29

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  • DOI: https://doi.org/10.1007/978-981-32-9949-8_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9948-1

  • Online ISBN: 978-981-32-9949-8

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