The Royal Society;England: The Royal Society Publishing
摘要:
摘要： Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. 其他題名： Phil. Trans. R. Soc. A 其他題名： Philos Trans A Math Phys Eng Sci 出版者： England: The Royal Society Publishing 出版日期： 2016-04-13 出處： Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences, 2016-04, Vol.374 (2065), p.20150204-20150204 資源來源： JSTOR Life Sciences Collection 版權： 2016 The Author(s) 版權： 2016 The Author(s). 識別號： ISSN: 1364-503X 識別號： ISSN: 1471-2962 識別號： EISSN: 1471-2962 識別號： DOI: 10.1098/rsta.2015.0204 識別號： PMID: 26953181
