{"title":"生理时间序列复杂波动的自适应数据分析。","authors":"C-K Peng, Madalena Costa, Ary L Goldberger","doi":"10.1142/S1793536909000035","DOIUrl":null,"url":null,"abstract":"<p><p>We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.</p>","PeriodicalId":88351,"journal":{"name":"Advances in adaptive data analysis","volume":"1 1","pages":"61-70"},"PeriodicalIF":0.0000,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798133/pdf/nihms-132018.pdf","citationCount":"0","resultStr":"{\"title\":\"ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES.\",\"authors\":\"C-K Peng, Madalena Costa, Ary L Goldberger\",\"doi\":\"10.1142/S1793536909000035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.</p>\",\"PeriodicalId\":88351,\"journal\":{\"name\":\"Advances in adaptive data analysis\",\"volume\":\"1 1\",\"pages\":\"61-70\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798133/pdf/nihms-132018.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in adaptive data analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S1793536909000035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in adaptive data analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S1793536909000035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES.
We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.