Life-support system benefits from noise

Suki, Bélâ; Alencar, AM; Sujeer, MK; Lutchen, K.R.; Collins, John J.; Andrade, José S.; Ingenito, EP; Zapperi, Stefano; Stanley, H. Eugene

doi:10.1038/30130

Cited by 198 publications

(100 citation statements)

References 4 publications

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“…A recently described conceptual model has predicted that the addition of random noise to mechanical ventilation might improve gas exchange and prevent further lung injury, 35 a finding supported by some experimental data. 20,27 Whether a fractal pattern of respiratory variability, incorporating long-range correlations, will be of clinical value in further improving mechanical ventilation remains to be tested in experimental and clinical settings.…”

Section: Discussionmentioning

confidence: 62%

Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

Peng¹,

Mietus²,

Liu

et al. 2002

Annals of Biomedical Engineering

258

185

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Abstract-We sought to quantify the fractal scaling properties of human respiratory dynamics and determine whether they are altered with healthy aging and gender. Continuous respiratory datasets ͑obtained by inductive plethysmography͒ were collected from 40 healthy adults ͑10 young men, 10 young women, 10 elderly men, and 10 elderly women͒ during 120 min of spontaneous breathing. The interbreath interval ͑IBI͒ time series were extracted by a new algorithm and fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Under supine, resting, and spontaneous breathing conditions, both healthy young and elderly subjects had scaling exponents for the IBI time series that indicate long-range ͑fractal͒ correlations across multiple time scales. Furthermore, the scaling exponents ͑mean Ϯ SD͒ for the IBI time series were significantly (pϽ0.03) lower ͑indicating decreased correlations͒ in the healthy elderly male (0.60Ϯ0.08) compared to the young male (0.68Ϯ0.07), young female (0.70Ϯ0.07), and elderly female (0.67Ϯ0.06) subjects. These results provide evidence for fractal organization in physiologic human breathing cycle dynamics, and for their degradation in elderly men. These findings may have implications for modeling integrated respiratory control mechanisms, quantifying their changes in aging or disease, and assessing the outcome of interventions aimed toward restoring normal physiologic respiratory dynamics.

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Section: Discussionmentioning

confidence: 62%

Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

Peng¹,

Mietus²,

Liu

et al. 2002

Annals of Biomedical Engineering

258

185

View full text Add to dashboard Cite

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“…The gradual decline in aA ratio highlights the moderately severe respiratory disease evident in both ventilated groups whereas the elevated markers of injury highlight the susceptibility of the fragile preterm lung to mechanical ventilation. The improved arterial oxygenation and/or lung compliance [5,[8][9][10][11][12][13], and delivery of required V T s at lower mean airway pressure associated with use of VV compared to controlled mechanical ventilation [10] and the ARDS Network protocol [18] are partly due to stochastic processes that enhance airway opening and alveolar recruitment [7]. Given that airway branching is complete at 129 days gestation, recruitment in the preterm lamb lungs would most likely occur in a similar manner to that shown in adult models.…”

Section: Discussionmentioning

confidence: 99%

“…In theoretical [7] and adult animal models of healthy [8,9] and injured or diseased lungs [5,[10][11][12][13], VV improves oxygenation and enhances gas exchange compared to CV. The potential applications and benefits of VV in the immature preterm lung are unstudied.…”

Section: Introductionmentioning

confidence: 99%

Variable ventilation improves ventilation and lung compliance in preterm lambs

et al. 2011

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“…Thus, our results are consistent with empirical evidence suggesting that development and maintenance of healthy function may require adjusting the number of connections. Last, the model may also provide a robust way to generate fluctuations that closely resemble physiologic signals, which could be implemented in medical devices, such as mechanical ventilators (32,33) and hormone infusion pumps (34). …”

Section: Discussionmentioning

confidence: 99%

Emergence of complex dynamics in a simple model of signaling networks

Amaral

Dı́az-Guilera

Moreira

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

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Various physical, social, and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by 1͞f scaling of their power spectra, can emerge in networks of simple signaling units. We found that, under general conditions, complex dynamics can be generated by systems fulfilling the following two requirements, (i) a ''small-world'' topology and (ii) the presence of noise. Our findings support two notable conclusions. First, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions i and ii are robust to some degree of degradation (i.e., they will still be able to generate 1͞f dynamics). C omplex systems are typically composed of interacting units that communicate information and are able to process and withstand a broad range of stresses (1-4). In physiology, freerunning healthy systems typically generate complex output signals that have long-range correlations [i.e., a 1͞f decay of the power spectra for low frequencies (**, 5-7)]. Deviations from the 1͞f pattern have been associated with disease or aging in various contexts (3,8).Despite its practical and fundamental interest (9), the origin of such correlated dynamics remains an unsolved problem (4). Until recently, attention has focused primarily on the complexity of the specific physiologic subsystems or on the nature of the nonlinear interactions between them (10-12). In particular, Boolean variables (which can take one of two values, 0 or 1) and Boolean functions have been extensively used to model the state and dynamics of complex systems (see ref. 12 for an introduction). The reason such a ''simplistic'' description may be appropriate arises from the fact that Boolean variables provide good approximations to the nonlinear functions encountered in many control systems (10,(13)(14)(15). Random Boolean networks (RBNs) were proposed by Kauffman (10) as models of genetic regulatory networks, and they have also been studied in other contexts (13,14). Wolfram (15), in contrast, proposed that cellular automata models, which are a class of ordered Boolean networks with identical units, may explain the real-word complexity. Neither of these two classes of models has been shown to generate the complex dynamics with 1͞f fluctuations observed in healthy physiologic systems.Here, we propose a modeling approach (Fig. 1a) that departs from traditional approaches in that we pay special attention to the topology of the network of interactions (4) and the role of noise (16). Our model is rooted in the following two considerations that are observed frequently in real-world systems. (i) The unit...

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Life-support system benefits from noise

Cited by 198 publications

References 4 publications

Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

Variable ventilation improves ventilation and lung compliance in preterm lambs

Emergence of complex dynamics in a simple model of signaling networks

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