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2020 | OriginalPaper | Chapter

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

Authors : Rekh Ram Janghel, Archana Verma, Yogesh Kumar Rathore

Published in: Data Management, Analytics and Innovation

Publisher: Springer Singapore

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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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Metadata
Title
Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal
Authors
Rekh Ram Janghel
Archana Verma
Yogesh Kumar Rathore
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9949-8_29