{"title":"一种分析帕金森病早期脑电图波列的方法","authors":"O. Sushkova, A. Morozov, A. Gabova","doi":"10.1109/BSB.2016.7552163","DOIUrl":null,"url":null,"abstract":"A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"A method of analysis of EEG wave trains in early stages of Parkinson's disease\",\"authors\":\"O. Sushkova, A. Morozov, A. Gabova\",\"doi\":\"10.1109/BSB.2016.7552163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.\",\"PeriodicalId\":363820,\"journal\":{\"name\":\"2016 International Conference on Bioinformatics and Systems Biology (BSB)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Bioinformatics and Systems Biology (BSB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BSB.2016.7552163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSB.2016.7552163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A method of analysis of EEG wave trains in early stages of Parkinson's disease
A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.
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