Computer-Aided Diagnosis System for Investigation and Detection of Epilepsy Using Machine Learning Techniques

  • J. NarenEmail author
  • A. B. Sarada Pyngas
  • S. Subhiksha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)


Epilepsy, a neurological disorder seems to be very chronic and affects a large number of people. Epilepsy is the third most common disease among all other mental disorders whose identification and cure is very hard. In the proposed work, a thorough investigation of the various techniques employed in various papers pertaining to Epilepsy detection and cure using Machine Learning have been taken and analysed. Various EEG signal preprocessing techniques and machine learning classifiers used in many papers were analysed with samples and conclusion on preprocessing techniques combined with classifiers gives the most accurate prediction of the disorder for preliminary investigation. Various classifiers, namely SVM, Random Forest, Neural Network, Logistic Regression were used on the dataset. The Random Forest Classifier gave the most accurate result. The accuracy was 58.9%.


EEG signal Seizure Classifiers Signal preprocessing Epilepsy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.SASTRA Deemed UniversityTirumalaisamudram, ThanjavurIndia

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