A short daytime test using correlation dimension for respiratory movement in OSAHS

Miyata, Masanori; Burioka, Naoto; Sako, Takanori; Suyama, Hisashi; Fukuoka, Yasushi; Tomita, Kazuhide; Higami, Shigeru; Shimizu, Eiji

doi:10.1183/09031936.04.00044104

Cited by 18 publications

(13 citation statements)

References 32 publications

(39 reference statements)

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“…Normocapnic resting ventilation was recorded during both sessions, but only the first recording was retained for (61) CD Surrogate data Burioka et al (7) CD Surrogate data Sako et al (53) CD Surrogate data El Khatib et al (17) CD Kolmogorov-Sinai Entropy Burioka et al (9) ApEn CD Surrogate data BuSha and Stella (10) ApEn Surrogate data Yeragani et al (74) ApEn LLE Suyama et al (65) CD Surrogate data Burioka et al (8) ApEn Surrogate data Thibault et al (68) LLE Miyata et al (36) CD Surrogate data Wysocki et al (72) Noise titration LLE CD Numbers in parentheses is the reference number. CD, correlation dimension; LLE, largest Lyapunov exponent; ApEn, approximate entropy.…”

Section: Methodsmentioning

confidence: 99%

Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans

Fiamma¹,

Straus²,

Thibault³

et al. 2007

American Journal of Physiology-Regulatory, Integrative and Comp

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In humans, lung ventilation exhibits breath-to-breath variability and dynamics that are nonlinear, complex, sensitive to initial conditions, unpredictable in the long-term, and chaotic. Hypercapnia, as produced by the inhalation of a CO(2)-enriched gas mixture, stimulates ventilation. Hypocapnia, as produced by mechanical hyperventilation, depresses ventilation in animals and in humans during sleep, but it does not induce apnea in awake humans. This emphasizes the suprapontine influences on ventilatory control. How cortical and subcortical commands interfere thus depend on the prevailing CO(2) levels. However, CO(2) also influences the variability and complexity of ventilation. This study was designed to describe how this occurs and to test the hypothesis that CO(2) chemoreceptors are important determinants of ventilatory dynamics. Spontaneous ventilatory flow was recorded in eight healthy subjects. Breath-by-breath variability was studied through the coefficient of variation of several ventilatory variables. Chaos was assessed with the noise titration method (noise limit) and characterized with numerical indexes [largest Lyapunov exponent (LLE), sensitivity to initial conditions; Kolmogorov-Sinai entropy (KSE), unpredictability; and correlation dimension (CD), irregularity]. In all subjects, under all conditions, a positive noise limit confirmed chaos. Hypercapnia reduced breathing variability, increased LLE (P = 0.0338 vs. normocapnia; P = 0.0018 vs. hypocapnia), increased KSE, and slightly reduced CD. Hypocapnia increased variability, decreased LLE and KSE, and reduced CD. These results suggest that chemoreceptors exert a strong influence on ventilatory variability and complexity. However, complexity persists in the quasi-absence of automatic drive. Ventilatory variability and complexity could be determined by the interaction between the respiratory central pattern generator and suprapontine structures.

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Section: Methodsmentioning

confidence: 99%

Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans

Fiamma¹,

Straus²,

Thibault³

et al. 2007

American Journal of Physiology-Regulatory, Integrative and Comp

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“…Breathing patterns are altered by the onset and progression of respiratory diseases (Kuratomi et al 1985; Brack et al 2002; Miyata et al 2002; Yeragani et al 2002; Bien et al 2004; Giraldo et al 2004; Miyata et al 2004; Casaseca-de-la-Higuera et al 2006; Wysocki et al 2006; Ibrahim et al 2008; Veiga et al 2010). The mechanisms responsible for changes in the breathing pattern in ALI and ARDS are attributed to the fundamental pathophysiology of diffuse pulmonary infiltrates, altered ventilation-perfusion matching, progressive hypoxemia, reduced lung compliance and increased work of breathing (Gattinoni et al 1994; Pelosi et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Jacono¹,

Mayer²,

Hsieh³

et al. 2011

Respiratory Physiology & Neurobiology

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Acute lung injury evokes a pulmonary inflammatory response and changes in the breathing pattern. The inflammatory response has a centrally mediated component which depends on the vagi. We hypothesize that the central inflammatory response, complimentary to the pulmonary inflammatory response, is expressed in the nuclei tractus solitarii (nTS) and that the expression of cytokines in the nTS is associated with breathing pattern changes. Adult, male Sprague-Dawley rats (n=12) received intratracheal instillation of either bleomycin (3U in 120 µl of saline) or saline (120 µl). Respiratory pattern changed by 24h. At 48 h, bronchoalveolar lavage fluid and lung tissue had increased IL-1β and TNF-α levels, but not IL-6. No changes in these cytokines were noted in serum. Immunocytochemical analysis of the brainstem indicated increased expression of IL-1β in the nTS commissural subnucleus that was localized to neurons. We conclude that breathing pattern changes in acute lung injury were associated with increased levels of IL-1β in brainstem, areas which integrate cardio-respiratory sensory input.

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“…Previous studies have characterized ventilation with certain indicators of nonlinear determinism or complexity (see Donaldson, 1992;Hughson et al, 1995;Fortrat et al, 1997;Small et al, 1999;Burioka et al, 2002;Miyata et al, 2002;Yeragani et al, 2002;Akay et al, 2003;Akay and Sekine, 2004;Baldwin et al, 2004;Miyata et al, 2004). However, such descriptors are typically highly sensitive to noise, making them particularly misleading when applied to short and noisy biological time series (Barahona and Poon, 1996;Bruce, 1996).…”

Section: Introductionmentioning

confidence: 99%

Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration

Wysocki

Fiamma

Straus

et al. 2006

Respiratory Physiology & Neurobiology

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The mammalian ventilatory behaviour exhibits nonlinear dynamics as reflected by certain nonlinearity or complexity indicators (e.g. correlation dimension, approximate entropy, Lyapunov exponents...) but this is not sufficient to determine its possible chaotic nature. To address this, we applied the noise titration technique, previously shown to discern and quantify chaos in short and noisy time series, to ventilatory flow recordings obtained in quietly breathing normal humans. Nine subjects (8 men and 1 woman, 24-42 yrs) were studied during 15-minute epochs of ventilatory steadystate (10.1±3.0 breaths/minute, tidal volume 0.63±0.2L). Noise titration applied to the unfiltered signals subsampled at 5 Hz detected nonlinearity in all cases (noise limit 20.2±12.5%). Noise limit values were weakly correlated to the correlation dimension and the largest Lyapunov exponent of the signals. This study shows that the noise titration approach evidences a chaotic dimension to the behavior of ventilatory flow over time in normal humans during tidal breathing.

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A short daytime test using correlation dimension for respiratory movement in OSAHS

Cited by 18 publications

References 32 publications

Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans

Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration

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