{"title":"Performance Analysis of Epileptic Seizure Detection System Using Neural Network Approach","authors":"R. Vaitheeshwari, V. SathieshKumar","doi":"10.1109/ICCIDS.2019.8862158","DOIUrl":null,"url":null,"abstract":"In recent years, numerous people are affected by a common neurological disorder called Epilepsy or Epileptic seizure. It occurs abruptly without any symptoms and thus increases the mortality rate of the humans. In order to warn the patient prior to the onset of seizure, a reliable warning system is needed. Thus the proposed research work aim to create an artificial neural network model to detect and predict the seizure event before its onset. The proposed Artificial Neural Network model is simple and efficient architecture that predict and detect the seizure event at the sensitivity rate of 91.15%. Experimental testing of the data show that prediction accuracy is 91% with considerable amount of computation time (630 seconds).","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIDS.2019.8862158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
In recent years, numerous people are affected by a common neurological disorder called Epilepsy or Epileptic seizure. It occurs abruptly without any symptoms and thus increases the mortality rate of the humans. In order to warn the patient prior to the onset of seizure, a reliable warning system is needed. Thus the proposed research work aim to create an artificial neural network model to detect and predict the seizure event before its onset. The proposed Artificial Neural Network model is simple and efficient architecture that predict and detect the seizure event at the sensitivity rate of 91.15%. Experimental testing of the data show that prediction accuracy is 91% with considerable amount of computation time (630 seconds).