S. Sargolzaei, M. Cabrerizo, M. Goryawala, A. S. Eddin, M. Adjouadi
{"title":"基于图分析的头皮脑电图功能连接网络用于癫痫分类","authors":"S. Sargolzaei, M. Cabrerizo, M. Goryawala, A. S. Eddin, M. Adjouadi","doi":"10.1109/SPMB.2013.6736779","DOIUrl":null,"url":null,"abstract":"The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<;0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Functional connectivity network based on graph analysis of scalp EEG for epileptic classification\",\"authors\":\"S. Sargolzaei, M. Cabrerizo, M. Goryawala, A. S. Eddin, M. Adjouadi\",\"doi\":\"10.1109/SPMB.2013.6736779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<;0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.\",\"PeriodicalId\":182231,\"journal\":{\"name\":\"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPMB.2013.6736779\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPMB.2013.6736779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Functional connectivity network based on graph analysis of scalp EEG for epileptic classification
The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<;0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.