{"title":"基于同步度的癫痫早期检测","authors":"Y. N. Baakek, S. Debbal","doi":"10.47363/JMHC/2020(2)133","DOIUrl":null,"url":null,"abstract":"In this work, the synchronization degree is calculated using bi-spectral analysis in order to distinguish between three sets of electroencephalogram signals: normal, pre-ictal, and epileptic seizure cases. The obtained results are compared to six parameters which also extracted from the same analysis; such as the entropies, the mean of bi-spectral amplitude, and weighted center of the bi-spectrum. The obtained results using the proposed algorithm are very satisfactory compared to the other parameters, and show that th","PeriodicalId":93468,"journal":{"name":"Journal of medicine and healthcare","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early Detection Of Epilepsy Based On Synchronization Degree\",\"authors\":\"Y. N. Baakek, S. Debbal\",\"doi\":\"10.47363/JMHC/2020(2)133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, the synchronization degree is calculated using bi-spectral analysis in order to distinguish between three sets of electroencephalogram signals: normal, pre-ictal, and epileptic seizure cases. The obtained results are compared to six parameters which also extracted from the same analysis; such as the entropies, the mean of bi-spectral amplitude, and weighted center of the bi-spectrum. The obtained results using the proposed algorithm are very satisfactory compared to the other parameters, and show that th\",\"PeriodicalId\":93468,\"journal\":{\"name\":\"Journal of medicine and healthcare\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medicine and healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47363/JMHC/2020(2)133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medicine and healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47363/JMHC/2020(2)133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Early Detection Of Epilepsy Based On Synchronization Degree
In this work, the synchronization degree is calculated using bi-spectral analysis in order to distinguish between three sets of electroencephalogram signals: normal, pre-ictal, and epileptic seizure cases. The obtained results are compared to six parameters which also extracted from the same analysis; such as the entropies, the mean of bi-spectral amplitude, and weighted center of the bi-spectrum. The obtained results using the proposed algorithm are very satisfactory compared to the other parameters, and show that th