Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis
| Autor: | Riccardo Barbieri, Emery N. Brown, Elisabeth Bloch-Salisbury, Frank Bednarek, David Paydarfar, Premananda Indic |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Brown, Emery N., Barbieri, Riccardo |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
medicine.medical_specialty
Pediatrics Respiratory rate Cardiography Surrogate data analysis Cardiovascular Cardiography Impedance Article Surrogate data Electrocardiography Models Heart Rate Pregnancy Internal medicine Medicine Heart rate variability Humans Vagal tone Premature medicine.diagnostic_test business.industry Respiration Bivariate analysis Coherence Preterm infants Female Infant Newborn Infant Premature Models Cardiovascular Pediatrics Perinatology and Child Health Obstetrics and Gynecology Impedance Infant Perinatology and Child Health Newborn Impedance cardiography Periodic breathing Breathing Cardiology business |
| Zdroj: | PMC |
| Popis: | Background: Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. Methods: We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Results: Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Conclusions: Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. Center for Integration of Medicine and Innovative Technology (U.S. Army Medical Research Acquisition Activity Cooperative Agreement W81XWH-07-2-0011) National Institutes of Health (U.S.) (Grant R01-HL084502) National Institutes of Health (U.S.) (Grant R01-DA015644) National Institutes of Health (U.S.) (Grant DP1-OD003646) |
| Databáze: | OpenAIRE |
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