Iqra Tahir, Usman Qamar, Hassan Abbas, Babar Zeb, Sana Abid
{"title":"基于群优化训练神经网络的脑电信号分类癫痫识别","authors":"Iqra Tahir, Usman Qamar, Hassan Abbas, Babar Zeb, Sana Abid","doi":"10.1145/3318299.3318374","DOIUrl":null,"url":null,"abstract":"EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"360 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Classification of EEG Signal by Training Neural Network with Swarm Optimization for Identification of Epilepsy\",\"authors\":\"Iqra Tahir, Usman Qamar, Hassan Abbas, Babar Zeb, Sana Abid\",\"doi\":\"10.1145/3318299.3318374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.\",\"PeriodicalId\":164987,\"journal\":{\"name\":\"International Conference on Machine Learning and Computing\",\"volume\":\"360 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Machine Learning and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3318299.3318374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Machine Learning and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3318299.3318374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of EEG Signal by Training Neural Network with Swarm Optimization for Identification of Epilepsy
EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.