{"title":"精神分裂症脑电数据的深度神经网络分类","authors":"Zhifen Guo, Lezhou Wu, Yun Li, Beilin Li","doi":"10.1109/DDCLS52934.2021.9455509","DOIUrl":null,"url":null,"abstract":"Schizophrenia(SZ) is a disease of unknown etiology and pathogenesis and is ranked by the World Health Organization as one of the top ten diseases contributing to the global burden of disease. Studying the internal physiological differences between EEG of schizophrenia patients and normal individuals is important for diagnosing and treating schizophrenia in order to determine objective physiological diagnostic criteria. The EEG data of patients with schizophrenia were preprocessed and markers were extracted. The convolutional neural network was used to characterize the difference of distributed structure of data for classification and the classification results were given. The accuracy of the classification was 92%, and the disease classification was effectively performed using deep learning networks.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Deep neural network classification of EEG data in schizophrenia\",\"authors\":\"Zhifen Guo, Lezhou Wu, Yun Li, Beilin Li\",\"doi\":\"10.1109/DDCLS52934.2021.9455509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Schizophrenia(SZ) is a disease of unknown etiology and pathogenesis and is ranked by the World Health Organization as one of the top ten diseases contributing to the global burden of disease. Studying the internal physiological differences between EEG of schizophrenia patients and normal individuals is important for diagnosing and treating schizophrenia in order to determine objective physiological diagnostic criteria. The EEG data of patients with schizophrenia were preprocessed and markers were extracted. The convolutional neural network was used to characterize the difference of distributed structure of data for classification and the classification results were given. The accuracy of the classification was 92%, and the disease classification was effectively performed using deep learning networks.\",\"PeriodicalId\":325897,\"journal\":{\"name\":\"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS52934.2021.9455509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS52934.2021.9455509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep neural network classification of EEG data in schizophrenia
Schizophrenia(SZ) is a disease of unknown etiology and pathogenesis and is ranked by the World Health Organization as one of the top ten diseases contributing to the global burden of disease. Studying the internal physiological differences between EEG of schizophrenia patients and normal individuals is important for diagnosing and treating schizophrenia in order to determine objective physiological diagnostic criteria. The EEG data of patients with schizophrenia were preprocessed and markers were extracted. The convolutional neural network was used to characterize the difference of distributed structure of data for classification and the classification results were given. The accuracy of the classification was 92%, and the disease classification was effectively performed using deep learning networks.