The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Ary L. Goldberger - One of the best experts on this subject based on the ideXlab platform.

  • Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics.
    Frontiers in physiology, 2017
    Co-Authors: Madalena D. Costa, Roger B. Davis, Ary L. Goldberger
    Abstract:

    Background: Short-term heart rate variability (HRV) is most commonly attributed to physiologic vagal tone modulation. However, with aging and cardiovascular disease, the emergence of high short-term HRV, consistent with the breakdown of the neuroautonomic-electrophysiologic control system, may confound traditional HRV analysis. An apparent dynamical signature of such anomalous short-term HRV is frequent changes in heart rate acceleration sign, defined here as heart rate fragmentation. Objective: The aims were to: 1) introduce a set of metrics designed to probe the degree of sinus rhythm fragmentation; 2) test the hypothesis that the degree of fragmentation of heartbeat time series increases with the participants' age in a group of healthy subjects; 3) test the hypothesis that the heartbeat time series from patients with advanced coronary artery disease (CAD) are more fragmented than those from healthy subjects; and 4) compare the performance of the new fragmentation metrics with standard time and frequency domain measures of short-term HRV. Methods: We analyzed annotated, open-access Holter recordings (University of Rochester Holter Warehouse) from healthy subjects and patients with CAD using these newly introduced metrics of heart rate fragmentation, as well as standard time and frequency domain indices of short-term HRV, detrended fluctuation analysis and sample entropy. Results: The degree of fragmentation of cardiac Interbeat Interval time series increased significantly as a function of age in the healthy population as well as in patients with CAD. Fragmentation was higher for the patients with CAD than the healthy subjects. Heart rate fragmentation metrics outperformed traditional short-term HRV indices, as well as two widely used nonlinear measures, sample entropy and detrended fluctuation analysis short-term exponent, in distinguishing healthy subjects and patients with CAD. The same level of discrimination was obtained from the analysis of normal-to-normal sinus (NN) and cardiac Interbeat Interval (RR) time series. Conclusion: The fragmentation framework and accompanying metrics introduced here constitute a new way of assessing short-term HRV under free-running conditions, one which appears to overcome salient limitations of traditional HRV analysis. Fragmentation of sinus rhythm cadence may provide new dynamical biomarkers for probing the integrity of the neuroautonomic-electrophysiologic network controlling the heartbeat in health and disease.

  • Multiscale Poincaré plots for visualizing the structure of heartbeat time series
    BMC medical informatics and decision making, 2016
    Co-Authors: Teresa Henriques, Sara Mariani, Anton Burykin, Filipa Rodrigues, Tiago F. Silva, Ary L. Goldberger
    Abstract:

    Background Poincare delay maps are widely used in the analysis of cardiac Interbeat Interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincare (MSP) plots.

  • Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures
    Cardiovascular Engineering, 2008
    Co-Authors: Madalena D. Costa, Chung-kang Peng, Ary L. Goldberger
    Abstract:

    Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and non-equilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools––multiscale entropy and multiscale time irreversibility––are able to extract information from cardiac Interbeat Interval time series not contained in traditional methods based on mean, variance or Fourier spectrum (two-point correlation) techniques. These new methods, with careful attention to their limitations, may be useful in diagnostics, risk stratification and detection of toxicity of cardiac drugs.

  • Statistical physics approach to categorize biologic signals: from heart rate dynamics to DNA sequences.
    Chaos (Woodbury N.Y.), 2007
    Co-Authors: Chung-kang Peng, Albert C. Yang, Ary L. Goldberger
    Abstract:

    We recently proposed a novel approach to categorize information carried by symbolic sequences based on their usage of repetitive patterns. A simple quantitative index to measure the dissimilarity between two symbolic sequences can be defined. This information dissimilarity index, defined by our formula, is closely related to the Shannon entropy and rank order of the repetitive patterns in the symbolic sequences. Here we discuss the underlying statistical physics assumptions of this dissimilarity index. We use human cardiac Interbeat Interval time series and DNA sequences as examples to illustrate the applicability of this generic approach to real-world problems.

  • Monofractal and Multifractal Approaches to Complex Biomedical Signals
    AIP Conference Proceedings, 2000
    Co-Authors: H E Stanley, Ary L. Goldberger, P. Ch. Ivanov, L A Amaral, S Havlin, C.-k. Peng
    Abstract:

    Even under healthy, basal conditions, physiologic systems show erratic fluctuations resembling those found in dynamical systems driven away from an equilibrium state. Do such “nonequilibrium” fluctuations simply reflect the fact that physiologic systems are being constantly perturbed by external and intrinsic uncorrelated noise? Or, do these fluctuations actually contain “hidden” information about the underlying nonequilibrium control mechanisms? We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. Specifically, we focus on Interbeat Interval variability as an important quantity to help elucidate possibly nonhomeostatic physiologic variability because (i) the heart rate is under direct neuroautonomic control, (ii) Interbeat Interval variability is readily measured by noninvasive means, and (iii) analysis of these heart rate dynamics may provide important practical diagnostic and prognostic information not obtainable with current approaches. The analytic tools we discuss may be used on a wider range of physiologic signals. We first review recent progress using two analysis methods—detrended fluctuation analysis and wavelets—appropriate for quantifying monofractal structures. We then describe very recent work that quantifies multifractal features of Interbeat Interval series, and the discovery that the multifractal structure of healthy subjects is different from that of diseased subjects. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the scaling exponent α is smaller during sleep periods compared to wake periods.

Pasi Fränti - One of the best experts on this subject based on the ideXlab platform.

  • Detection of time irreversibility in Interbeat Interval time series by visible and nonvisible motifs from horizontal visibility graph
    Biomedical Signal Processing and Control, 2020
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Pasi Fränti
    Abstract:

    Abstract The time irreversibility is a characteristic feature of biological systems and its presence in heart rate (HR) is due to the complex dynamical process involved in the controlling mechanism of cardiovascular system (CVS). In this study, we propose a novel method referred to time irreversibility using visibility motifs (TIVM) for quantifying temporal asymmetry by extracting visible and non-visible horizontal visibility graph (HVG) motifs from a time series. The method can be applied using two simple approaches for transforming original time series into visible and non-visible motifs without mapping the time series into complex networks. Kullback-Leibler divergence (KLD) is used to quantify the temporal asymmetry between visible and non-visible HVG motifs of a time series. First, we explore the structural relation between HVG motifs and ordinal patterns reveal that motifs with different structures can have the similar visibility level. Next, we apply the method to find the asymmetry in different synthetic signals and real world Interbeat Interval (IBI) time series from healthy and pathological subjects. The findings reveal that the proposed method provide more accurate information about the healthy biological systems and changes occurring due to aging or disease. It is an effective tool for discriminating healthy young, elderly and pathological groups.

  • Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph
    IEEE Access, 2019
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Ishtiaq Rasool Khan, Susanto Rahardja, Pasi Fränti
    Abstract:

    Horizontal visibility graph (HVG) motifs have been recently introduced to analyze the dynamical information encoded by biological signals. However, the result of the analysis strongly depends on the selected window size of the motifs. Different sizes ranging from 3 to 5 have been previously used, but such small window sizes are insufficient to cope with the complexity of biological systems and often fail to extract salient features of the encoded information. It is known that larger window size increases the total number of possible motifs, and it leads to the distribution of the statistics into too many motifs, which causes each individual motif to contain too little information and make it even more difficult to reliably detect system dynamics. To resolve this problem, we group the motifs based on the number of edges. Using the grouped motifs, we propose grouped horizontal visibility entropy (GHVE) to quantify the complexity based on the probability distribution of the observations within these groups. We apply GHVE to quantify the complexity of simulated white and 1/f noise. The results reveal that the 1/f noise time series exhibits a higher complexity than white noise time series, which indicates that the 1/f noise is structurally more complex than white Gaussian noise. We apply the method for analyzing Interbeat Intervals time series. The results show that the proposed GHVE measure is more accurate in distinguishing healthy and pathological subjects than its non-grouped counter-part HVG. It is, therefore, better suited to detect changes in aging, disease severity, and activity levels (sleep and wake period).

Wajid Aziz - One of the best experts on this subject based on the ideXlab platform.

  • Detection of time irreversibility in Interbeat Interval time series by visible and nonvisible motifs from horizontal visibility graph
    Biomedical Signal Processing and Control, 2020
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Pasi Fränti
    Abstract:

    Abstract The time irreversibility is a characteristic feature of biological systems and its presence in heart rate (HR) is due to the complex dynamical process involved in the controlling mechanism of cardiovascular system (CVS). In this study, we propose a novel method referred to time irreversibility using visibility motifs (TIVM) for quantifying temporal asymmetry by extracting visible and non-visible horizontal visibility graph (HVG) motifs from a time series. The method can be applied using two simple approaches for transforming original time series into visible and non-visible motifs without mapping the time series into complex networks. Kullback-Leibler divergence (KLD) is used to quantify the temporal asymmetry between visible and non-visible HVG motifs of a time series. First, we explore the structural relation between HVG motifs and ordinal patterns reveal that motifs with different structures can have the similar visibility level. Next, we apply the method to find the asymmetry in different synthetic signals and real world Interbeat Interval (IBI) time series from healthy and pathological subjects. The findings reveal that the proposed method provide more accurate information about the healthy biological systems and changes occurring due to aging or disease. It is an effective tool for discriminating healthy young, elderly and pathological groups.

  • Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph
    IEEE Access, 2019
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Ishtiaq Rasool Khan, Susanto Rahardja, Pasi Fränti
    Abstract:

    Horizontal visibility graph (HVG) motifs have been recently introduced to analyze the dynamical information encoded by biological signals. However, the result of the analysis strongly depends on the selected window size of the motifs. Different sizes ranging from 3 to 5 have been previously used, but such small window sizes are insufficient to cope with the complexity of biological systems and often fail to extract salient features of the encoded information. It is known that larger window size increases the total number of possible motifs, and it leads to the distribution of the statistics into too many motifs, which causes each individual motif to contain too little information and make it even more difficult to reliably detect system dynamics. To resolve this problem, we group the motifs based on the number of edges. Using the grouped motifs, we propose grouped horizontal visibility entropy (GHVE) to quantify the complexity based on the probability distribution of the observations within these groups. We apply GHVE to quantify the complexity of simulated white and 1/f noise. The results reveal that the 1/f noise time series exhibits a higher complexity than white noise time series, which indicates that the 1/f noise is structurally more complex than white Gaussian noise. We apply the method for analyzing Interbeat Intervals time series. The results show that the proposed GHVE measure is more accurate in distinguishing healthy and pathological subjects than its non-grouped counter-part HVG. It is, therefore, better suited to detect changes in aging, disease severity, and activity levels (sleep and wake period).

  • Studying the dynamics of Interbeat Interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis.
    PloS one, 2018
    Co-Authors: Imtiaz Ahmed Awan, Wajid Aziz, Imran Hussain Shah, Nazneen Habib, Jalal S. Alowibdi, Sharjil Saeed, Malik Sajjad Ahmed Nadeem, Syed Ahsin Ali Shah
    Abstract:

    Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as Interbeat Interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.

Gulraiz Iqbal Choudhary - One of the best experts on this subject based on the ideXlab platform.

  • Detection of time irreversibility in Interbeat Interval time series by visible and nonvisible motifs from horizontal visibility graph
    Biomedical Signal Processing and Control, 2020
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Pasi Fränti
    Abstract:

    Abstract The time irreversibility is a characteristic feature of biological systems and its presence in heart rate (HR) is due to the complex dynamical process involved in the controlling mechanism of cardiovascular system (CVS). In this study, we propose a novel method referred to time irreversibility using visibility motifs (TIVM) for quantifying temporal asymmetry by extracting visible and non-visible horizontal visibility graph (HVG) motifs from a time series. The method can be applied using two simple approaches for transforming original time series into visible and non-visible motifs without mapping the time series into complex networks. Kullback-Leibler divergence (KLD) is used to quantify the temporal asymmetry between visible and non-visible HVG motifs of a time series. First, we explore the structural relation between HVG motifs and ordinal patterns reveal that motifs with different structures can have the similar visibility level. Next, we apply the method to find the asymmetry in different synthetic signals and real world Interbeat Interval (IBI) time series from healthy and pathological subjects. The findings reveal that the proposed method provide more accurate information about the healthy biological systems and changes occurring due to aging or disease. It is an effective tool for discriminating healthy young, elderly and pathological groups.

  • Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph
    IEEE Access, 2019
    Co-Authors: Gulraiz Iqbal Choudhary, Wajid Aziz, Ishtiaq Rasool Khan, Susanto Rahardja, Pasi Fränti
    Abstract:

    Horizontal visibility graph (HVG) motifs have been recently introduced to analyze the dynamical information encoded by biological signals. However, the result of the analysis strongly depends on the selected window size of the motifs. Different sizes ranging from 3 to 5 have been previously used, but such small window sizes are insufficient to cope with the complexity of biological systems and often fail to extract salient features of the encoded information. It is known that larger window size increases the total number of possible motifs, and it leads to the distribution of the statistics into too many motifs, which causes each individual motif to contain too little information and make it even more difficult to reliably detect system dynamics. To resolve this problem, we group the motifs based on the number of edges. Using the grouped motifs, we propose grouped horizontal visibility entropy (GHVE) to quantify the complexity based on the probability distribution of the observations within these groups. We apply GHVE to quantify the complexity of simulated white and 1/f noise. The results reveal that the 1/f noise time series exhibits a higher complexity than white noise time series, which indicates that the 1/f noise is structurally more complex than white Gaussian noise. We apply the method for analyzing Interbeat Intervals time series. The results show that the proposed GHVE measure is more accurate in distinguishing healthy and pathological subjects than its non-grouped counter-part HVG. It is, therefore, better suited to detect changes in aging, disease severity, and activity levels (sleep and wake period).

Paulo S. Boggio - One of the best experts on this subject based on the ideXlab platform.

  • Ventrolateral but not Dorsolateral Prefrontal Cortex tDCS effectively impact emotion reappraisal - effects on Emotional Experience and Interbeat Interval.
    Scientific reports, 2018
    Co-Authors: Lucas Murrins Marques, Letícia Yumi Nakao Morello, Paulo S. Boggio
    Abstract:

    Emotions can be understood as behavioral, physiological, and subjective individual’s alteration due to a given situation. Several times, an efficient regulation of these emotions can promote psychological and social survival. It has been demonstrated that the Prefrontal Cortex (PFC) presents a relevant role in cognitive control, especially during emotion regulation strategies. However, evidence for the role of the PFC and emotional regulation comes mostly from neuroimaging experiments lacking from causal information. Transcranial Direct Current Stimulation (tDCS) has been shown to be an efficient noninvasive neuromodulation technique capable to address causal hypothesis. The aim of this study was to investigate the role of two regions of the PFC (Dorsolateral and Ventrolateral region) on different strategies of emotional reappraisal during the observation of negative images. 180 undergraduate students (mean age 21,75 ± 3,38) participated in this study, divided in two experiments (Dorsolateral PFC - n = 90; Ventrolateral PFC - n = 90). As not expected, DLPFC tDCS did not modulate the responses on the emotional regulation task. However, VLPFC tDCS resulted in less negative valence of negative images as well as decreased cardiac Interbeat Interval on earlier moments of emotional processing. These findings supports the general view about the role of the PFC on emotional regulation and, at the same time, advances the field by providing evidence that evaluation of negative stimuli is much more based on the VLPFC than on the DLPCF.

  • Ventrolateral but not Dorsolateral Prefrontal Cortex tDCS effectively impact emotion reappraisal – effects on Emotional Experience and Interbeat Interval
    Nature Publishing Group, 2018
    Co-Authors: Lucas Murrins Marques, Letícia Yumi Nakao Morello, Paulo S. Boggio
    Abstract:

    Abstract Emotions can be understood as behavioral, physiological, and subjective individual’s alteration due to a given situation. Several times, an efficient regulation of these emotions can promote psychological and social survival. It has been demonstrated that the Prefrontal Cortex (PFC) presents a relevant role in cognitive control, especially during emotion regulation strategies. However, evidence for the role of the PFC and emotional regulation comes mostly from neuroimaging experiments lacking from causal information. Transcranial Direct Current Stimulation (tDCS) has been shown to be an efficient noninvasive neuromodulation technique capable to address causal hypothesis. The aim of this study was to investigate the role of two regions of the PFC (Dorsolateral and Ventrolateral region) on different strategies of emotional reappraisal during the observation of negative images. 180 undergraduate students (mean age 21,75 ± 3,38) participated in this study, divided in two experiments (Dorsolateral PFC - n = 90; Ventrolateral PFC - n = 90). As not expected, DLPFC tDCS did not modulate the responses on the emotional regulation task. However, VLPFC tDCS resulted in less negative valence of negative images as well as decreased cardiac Interbeat Interval on earlier moments of emotional processing. These findings supports the general view about the role of the PFC on emotional regulation and, at the same time, advances the field by providing evidence that evaluation of negative stimuli is much more based on the VLPFC than on the DLPCF