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  • The Royal Society  (2)
  • General works  (2)
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  • The Royal Society  (2)
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  • General works  (2)
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  • 1
    Online Resource
    Online Resource
    The Royal Society ; 2016
    In:  Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Vol. 374, No. 2065 ( 2016-04-13), p. 20150206-
    In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, The Royal Society, Vol. 374, No. 2065 ( 2016-04-13), p. 20150206-
    Abstract: The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.
    Type of Medium: Online Resource
    ISSN: 1364-503X , 1471-2962
    RVK:
    Language: English
    Publisher: The Royal Society
    Publication Date: 2016
    detail.hit.zdb_id: 208381-4
    detail.hit.zdb_id: 1462626-3
    SSG: 11
    SSG: 5,1
    SSG: 5,21
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Online Resource
    Online Resource
    The Royal Society ; 2016
    In:  Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Vol. 374, No. 2065 ( 2016-04-13), p. 20150204-
    In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, The Royal Society, Vol. 374, No. 2065 ( 2016-04-13), p. 20150204-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1364-503X , 1471-2962
    RVK:
    Language: English
    Publisher: The Royal Society
    Publication Date: 2016
    detail.hit.zdb_id: 208381-4
    detail.hit.zdb_id: 1462626-3
    SSG: 11
    SSG: 5,1
    SSG: 5,21
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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