Common multifractality in the heart rate variability and brain activity of healthy humans

Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario M5B 2K3, Canada.
Chaos (Woodbury, N.Y.) (Impact Factor: 1.95). 06/2010; 20(2):023121. DOI: 10.1063/1.3427639
Source: PubMed


The influence from the central nervous system on the human multifractal heart rate variability (HRV) is examined under the autonomic nervous system perturbation induced by the head-up-tilt body maneuver. We conducted the multifractal factorization analysis to factor out the common multifractal factor in the joint fluctuation of the beat-to-beat heart rate and electroencephalography data. Evidence of a central link in the multifractal HRV was found, where the transition towards increased (decreased) HRV multifractal complexity is associated with a stronger (weaker) multifractal correlation between the central and autonomic nervous systems.

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    • "So far, the multifractal analysis shows to be suitable for investigating heartbeat dynamics [15]. Multifractal behaviour of the cardiovascular system is investigated through analysis of Hölder exponents and fractal behavior using different approaches, such as detrended fluctuation analysis (DFA) and self-affine fractal variability analysis of heartbeat dynamics [17] [18], local variability analysis [19] [20], analysis of healthy and pathological dynamics from the aspect of respiratory rate variability [21] and brain activity in healthy patients [21] [22], analysis of Hölder exponents using laser Doppler flowmetry technique [23], analysis of progressive central hypovolemia influence [24] [25], estimation of heart rate variability (HRV) large deviations [26], and arrhythmia [27]. Many variations of cardiosignals are examined using multifractality, age-related changes [28] [29], as well as changes in heart dynamics: before and after particular treatment [30], for different human races [31], body positions [23], and during wake and sleep phases [32] [33]. "
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    ABSTRACT: Phonocardiography has shown a great potential for developing low-cost computer-aided diagnosis systems for cardiovascular monitoring. So far, most of the work reported regarding cardiosignal analysis using multifractals is oriented towards heartbeat dynamics. This paper represents a step towards automatic detection of one of the most common pathological syndromes, so-called mitral valve prolapse (MVP), using phonocardiograms and multifractal analysis. Subtle features characteristic for MVP in phonocardiograms may be difficult to detect. The approach for revealing such features should be locally based rather than globally based. Nevertheless, if their appearances are specific and frequent, they can affect a multifractal spectrum. This has been the case in our experiment with the click syndrome. Totally, 117 pediatric phonocardiographic recordings (PCGs), 8 seconds long each, obtained from 117 patients were used for PMV automatic detection. We propose a two-step algorithm to distinguish PCGs that belong to children with healthy hearts and children with prolapsed mitral valves (PMVs). Obtained results show high accuracy of the method. We achieved 96.91% accuracy on the dataset (97 recordings). Additionally, 90% accuracy is achieved for the evaluation dataset (20 recordings). Content of the datasets is confirmed by the echocardiographic screening.
    Computational and Mathematical Methods in Medicine 05/2013; 2013:376152. DOI:10.1155/2013/376152 · 0.77 Impact Factor
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    • "Fractals are the building blocks of nature and are found everywhere. Examples of physiological fractal processes include fluctuations in the heartbeat and brain waves [7–9]. The importance of fractal analysis in physiological signals is that it allows one to gain a greater understanding of the underlying process and allows one to discern health from pathology [10]. "
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    ABSTRACT: Ocular monochromatic aberrations display dynamic behavior even when the eye is fixating on a stationary stimulus. The fluctuations are commonly characterized in the frequency domain using the power spectrum obtained via the Fourier transform. In this paper we used a wavelet-based multifractal analytical approach to provide a more in depth analysis of the nature of the aberration fluctuations. The aberrations of five subjects were measured at 21 Hz using an open-view Shack-Hartmann sensor. We show that the aberration dynamics are multifractal. The most frequently occurring Hölder exponent for the rms wavefront error, averaged across the five subjects, was 0.31 ± 0.10. This suggests that the time course of the aberration fluctuations is antipersistant. Future applications of multifractal analysis are discussed.
    Biomedical Optics Express 03/2011; 2(3):464-70. DOI:10.1364/BOE.2.000464 · 3.65 Impact Factor
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    ABSTRACT: In this study, we introduce a novel approach to examine the heart-brain interaction underlying the multifractality in the heart rate variability of healthy humans. Via the autonomic perturbation induced by the passive head-up-tilt (HUT), empirical supports for the central-autonomic fractal correlation (CAFC) are presented based on the fractal properties in simultaneously recorded electroencephalography (EEG) and heart beat interval data. In particular, we show the measure of CAFC varies according to the fractal complexity of HRV. The result implies the change towards more (less) complex HRV fractal complexity is associated with a stronger (weaker) CAFC and is tested significantly different from the surrogate data. Using the HRV and EEG physiological correlates in the frequency domain, we further show that CAFC is significantly linked to the EEG Beta band activities and the HRV spectral measures in the supine position. These findings suggest a potential arousal factor underlying the physiological processing of the central influence in the HRV scale-free dynamics.
    Frontiers in Physiology 01/2011; 2:123. DOI:10.3389/fphys.2011.00123 · 3.53 Impact Factor
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