Multiscale Analysis of Biological Data By Scale-Dependent Lyapunov Exponent
文献类型：期刊论文
| 作者 | Gao JB(高建波) ; Hu J; Tung WW; Blasch E
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| 刊名 | FRONTIERS IN PHYSIOLOGY
 |
| 出版日期 | 2012 |
| 卷号 | 2页码:1-13 |
| 通讯作者邮箱 | jbgao.pmb@gmail.com |
| 关键词 | multiscale analysis chaos random fractal scale-dependent Lyapunov exponent EEG heart-rate variability intermittent chaos non-stationarity |
| ISSN号 | 1664-042X |
| 产权排序 | [Gao, Jianbo] PMB Intelligence LLC, W Lafayette, IN 47996 USA; [Gao, Jianbo] Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing, Peoples R China; [Gao, Jianbo; Blasch, Erik] Wright State Univ, Dayton, OH 45435 USA; [Hu, Jing] Affymetrix Inc, Santa Clara, CA USA; [Tung, Wen-Wen] Purdue Univ, Dept Earth & Atmospher Sci, W Lafayette, IN 47907 USA |
| 通讯作者 | Gao, J (reprint author), PMB Intelligence LLC, POB 2077, W Lafayette, IN 47996 USA. |
| 中文摘要 | Physiological signals often are highly non-stationary (i.e., mean and variance change with time) and multiscaled (i.e., dependent on the spatial or temporal interval lengths). They may exhibit different behaviors, such as non-linearity, sensitive dependence on small disturbances, long memory, and extreme variations. Such data have been accumulating in all areas of health sciences and rapid analysis can serve quality testing, physician assessment, and patient diagnosis. To support patient care, it is very desirable to characterize the different signal behaviors on a wide range of scales simultaneously. The Scale-Dependent Lyapunov Exponent (SDLE) is capable of such a fundamental task. In particular, SDLE can readily characterize all known types of signal data, including deterministic chaos, noisy chaos, random 1/f(alpha) processes, stochastic limit cycles, among others. SDLE also has some unique capabilities that are not shared by other methods, such as detecting fractal structures from non-stationary data and detecting intermittent chaos. In this article, we describe SDLE in such a way that it can be readily understood and implemented by non-mathematically oriented researchers, develop a SDLE-based consistent, unifying theory for the multiscale analysis, and demonstrate the power of SDLE on analysis of heart-rate variability (HRV) data to detect congestive heart failure and analysis of electroencephalography (EEG) data to detect seizures. |
| 类目[WOS] | Physiology |
| 研究领域[WOS] | Physiology |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000209172900002 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/58580]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Gao JB,Hu J,Tung WW,et al. Multiscale Analysis of Biological Data By Scale-Dependent Lyapunov Exponent[J]. FRONTIERS IN PHYSIOLOGY,2012,2:1-13. |
| APA | 高建波,Hu J,Tung WW,&Blasch E.(2012).Multiscale Analysis of Biological Data By Scale-Dependent Lyapunov Exponent.FRONTIERS IN PHYSIOLOGY,2,1-13. |
| MLA | 高建波,et al."Multiscale Analysis of Biological Data By Scale-Dependent Lyapunov Exponent".FRONTIERS IN PHYSIOLOGY 2(2012):1-13. |
入库方式： OAI收割
来源：力学研究所
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