The cardiovascular system as coupled oscillators?

Stefanovska, Aneta; Lotrič, Maja Bračič; Strle, Sašo; Haken, Hermann

doi:10.1088/0967-3334/22/3/311

Cited by 68 publications

(87 citation statements)

References 32 publications

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“…The cardiovascular system has been modeled as coupled linear and nonlinear oscillators to study the time-varying features of characteristic frequencies [8]. Metabolic activity adjusts blood flow to meet the need of cells for oxygen.…”

Section: Discussionmentioning

confidence: 99%

“…This activity is dependent on cardiac and respiratory activities, which control the level of oxygenated blood flow. On the other hand, the constrictor activities of myogenic and neurogenic systems diminish blood transport, thus lowering metabolic activity [8]. Ping and Johnson found that the myogenic response could be enhanced by sympathetic tone [30].…”

Section: Discussionmentioning

confidence: 99%

“…Other underlying physiological mechanisms include neurogenic (0.02-0.05 Hz), myogenic (0.05-0.15 Hz), respiratory (0.15-0.40 Hz), and cardiac (0.4-2.0 Hz) origins. However, the characteristic frequencies embedded in blood flow oscillations (BFO) show a time-varying feature that cannot be fully characterized by linear methods, such as wavelet and Fourier transforms [8][9][10]. This time-varying feature of characteristic frequencies may be due to the interactions of each control mechanism [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…However, the characteristic frequencies embedded in blood flow oscillations (BFO) show a time-varying feature that cannot be fully characterized by linear methods, such as wavelet and Fourier transforms [8][9][10]. This time-varying feature of characteristic frequencies may be due to the interactions of each control mechanism [7][8][9]. Stefanovska et al proposed a model consisting of linear and nonlinear oscillators to understand this phenomenon [8].…”

Section: Introductionmentioning

confidence: 99%

“…This time-varying feature of characteristic frequencies may be due to the interactions of each control mechanism [7][8][9]. Stefanovska et al proposed a model consisting of linear and nonlinear oscillators to understand this phenomenon [8]. Based on this model, the variations in frequency and amplitude of a periodic component are due to the interactions among the control mechanisms of BFO.…”

Section: Introductionmentioning

confidence: 99%

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Using multifractal detrended fluctuation analysis to assess sacral skin blood flow oscillations in people with spinal cord injury

Liao¹,

Jan²

2011

JRRD

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Abstract-The purpose of this study was to investigate whether the multifractal detrended fluctuation analysis (MDFA) of skin blood flow oscillations (BFO) differed between nondisabled controls and people with spinal cord injury (SCI). The study of skin BFO has shown promise for assessing blood flow control mechanisms and risk for pressure ulcers. We recruited 23 subjects, including 11 people with SCI and 12 nondisabled controls. Thermally induced maximal sacral skin BFO were measured by laser Doppler flowmetry. MDFA was used to characterize nonlinear complexity of metabolic (0.0095 to 0.02 Hz), neurogenic (0.02 to 0.05 Hz), and myogenic (0.05 to 0.15 Hz) BFO. We found that maximal vasodilation was significantly smaller in people with SCI than in nondisabled controls (p < 0.05). MDFA showed that metabolic BFO exhibited less complexity in people with SCI (p < 0.05), neurogenic BFO exhibited less complexity in people with complete SCI (p < 0.05), and myogenic BFO did not show significant differences between people with SCI and nondisabled controls. This study demonstrated the feasibility of using the MDFA to characterize nonlinear complexity of BFO, which is related to vasodilatory functions in people with SCI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using multifractal detrended fluctuation analysis to assess sacral skin blood flow oscillations in people with spinal cord injury

Liao¹,

Jan²

2011

JRRD

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Cardiorespiratory dynamics during transitions between mechanical and spontaneous ventilation in intensive care

Burykin

Buchman

2008

Complexity

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Life support devices (e.g., heart pacemakers, ventilators, dialysis machines) are commonly thought to affect mainly their target organ system. This preliminary study attempts to determine the extent to which mechanical support of one organ system (lungs by mechanical ventilator) affects functions of other systems (heart and blood vessels). We studied changes in cardiorespiratory interactions and in dynamics of cardiovascular system during scheduled transitions (spontaneous breathing trials, SBT) between mechanical and spontaneous ventilation in critically ill patients. This initial study population consisted of 13 patients admitted to a surgical intensive care unit (ICU) following surgery, trauma, or complications who were judged candidates for liberation from the ventilator. We collected continuous respiratory, cardiac (ECG), and perfusion (blood pressure and pulse oximetry) signals of mechanically ventilated patients before, during, and after SBT. The data were analyzed using spectral analysis, phase dynamics, and entropy measures. We found that the mechanical ventilation not only drives lung dynamics but also affects the dynamics of cardiac and vascular systems. Spontaneous cardiovascular rhythms are entrained by the mechanical ventilator and are drawn into synchrony. Sudden interruption of mechanical ventilation typically leads to rapid desynchronization. This synchronization is restored upon reinstitution of mechanical ventilation. The initial data suggest that therapies intended to support one organ system may propagate unanticipated effects to other organ systems. Moreover, sustained therapies may disturb mechanisms that promote natural synchronization and variability and thereby adversely affect recovery of normal organ system interactions. We suggest that new measures and displays of synchronization not only could provide insight into the organorgan coupling but also could yield information to optimize the function of devices used to support the critically ill patient.

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