TITLE

Assessing multiscale complexity of short heart rate variability series through a model-based linear approach

AUTHOR(S)
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
PUB. DATE
September 2017
SOURCE
Chaos;2017, Vol. 27 Issue 9, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
ACCESSION #
125470861

 

Related Articles

  • Signal Processing and Sampling Method for Obtaining Time Series Corresponding to Higher Order Derivatives. Sterian, Andreea; Toma, Alexandru // Mathematical Problems in Engineering;2010, Vol. 2010, Special section p1 

    For modeling and controlling dynamic phenomena it is important to establish with higher accuracy some significant quantities corresponding to the dynamic system. For fast phenomena, such significant quantities are represented by the derivatives of the received signals. In case of advanced...

  • A novel modeling method based on Multi-dimensional Taylor Network and its application in time series prediction. Yi Lin; Hongsen Yan; Bo Zhou // Advanced Materials Research;2014, Vol. 940, p480 

    A novel modeling method based on multi-dimensional Taylor network is proposed. The structure and the principle of the multi-dimensional Taylor network are introduced. Based on this, the method is applied in the nonlinear time series prediction based on multi-dimensional Taylor network. It...

  • Resource-Efficient Data Gathering in Sensor Networks for Environment Reconstruction. LINGHE KONG; XIAO-YANG LIU; MEIXIA TAO; MIN-YOU WU; YU GU; LONG CHENG; JIANWEI NIU // Computer Journal;2015, Vol. 58 Issue 6, p1330 

    Environment reconstruction is to rebuild the physical environment in the cyberspace using the sensory data collected by sensor networks, which is a fundamental method for human to understand the physical world in depth. A lot of basic scientific work such as nature discovery and organic...

  • Phase inversion and collapse of cross-spectral function. Nelson, C. W.; Hati, A.; D. A. Howe // Electronics Letters;12/5/2013, Vol. 49 Issue 25, p1640 

    Cross-spectral analysis is a mathematical tool for extracting the power spectral density of a correlated signal from two time series in the presence of uncorrelated interfering signals. A set of conditions is demonstrated and explained where the detection of the desired signal using...

  • Numerical Methods for Fitting and Stimulating Autoregressive-to-Anything Processes. Cario, Marne C. // INFORMS Journal on Computing;Winter98, Vol. 10 Issue 1, p72 

    Discusses a numerical method for fitting an AutoRegressive-to-Anything (ARTA) process, a time series with arbitrary marginal distribution and autocorrelation structure. Implementation of ARTA in the software ARTAFACTS; Observations from ART processes for use as inputs to a computer simulation.

  • Detecting changes from short to long memory. Hassler, Uwe; Scheithauer, Jan // Statistical Papers;Oct2011, Vol. 52 Issue 4, p847 

    This paper studies well-known tests by Kim et al. (J Econom 109:389-392, 2002) and Busetti and Taylor (J Econom 123:33-66, 2004) for the null hypothesis of short memory against a change to nonstationarity, I (1). The potential break point is not assumed to be known but estimated from the data....

  • Mapping time series into complex networks based on equal probability division. Zhang, Zelin; Xu, Jinyu; Zhou, Xiao // AIP Advances;Jan2019, Vol. 9 Issue 1, pN.PAG 

    As effective representations of complex systems, complex networks have attracted scholarly attention for their many practical applications. They also represent a new tool for time series analysis. In order to characterize the underlying dynamic features, the structure of transformed networks...

  • Time series similarity matching based on weighted IMF. SUN Ru-ru; XIAO Di // Application Research of Computers / Jisuanji Yingyong Yanjiu;Dec2013, Vol. 30 Issue 12, p3664 

    Empirical mode decomposition (EMD) algorithm is very suitable for non-stable decomposition of the sequence of signals, nonlinear sequence signal, and complex signals with high noise ratio. Sequence signal after EMD decomposition the costs intrinsic mode functions (IMF) and the residual series...

  • Clustering multivariate time series based on Riemannian manifold. Jiancheng Sun // Electronics Letters;9/15/2016, Vol. 52 Issue 19, p1607 

    An approach for clustering multivariate time series (MTS) is presented in cases of variable length, noisy data or mix of different type variables. First the covariance matrices are estimated which is used as a feature to represent the MTS, then project the covariance matrices from a Riemannian...

  • Robust detection of periodic time series measured from biological systems. Ahdesmäki, Miika; Lähdesmäki, Harri; Pearson, Ron; Huttunen, Heikki; Yli-Harja, Olli // BMC Bioinformatics;2005, Vol. 6, p117 

    Background: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications....

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics