| Multiscale Analysis of Biological Data By Scale-Dependent Lyapunov Exponent | |
Gao JB(高建波) ; Hu J; Tung WW; Blasch E; Gao, J (reprint author), PMB Intelligence LLC, POB 2077, W Lafayette, IN 47996 USA.
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| Source Publication | FRONTIERS IN PHYSIOLOGY
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| 2012 | |
| Volume | 2Pages:1-13 |
| ISSN | 1664-042X |
| Abstract | 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. |
| Keyword | Multiscale Analysis Chaos Random Fractal Scale-dependent Lyapunov Exponent Eeg Heart-rate Variability Intermittent Chaos Non-stationarity |
| DOI | 10.3389/fphys.2011.00110 |
| Indexed By | SCI |
| Language | 英语 |
| WOS ID | WOS:000209172900002 |
| WOS Research Area | Physiology |
| WOS Subject | Physiology |
| Funding Organization | U.S. NSF [CMMI-1031958, 0826119] ; State Key Laboratory of Non-linear Mechanics (LNM), Institute of Mechanics, Chinese Academy of Sciences, Beijing, People's Republic of China |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/58580 |
| Collection | 非线性力学国家重点实验室 |
| Corresponding Author | Gao, J (reprint author), PMB Intelligence LLC, POB 2077, W Lafayette, IN 47996 USA. |
| Recommended Citation 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,&Gao, J .(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. |
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| JourArSuppl2016-012.（1176KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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