Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis
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...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2016
|
| Online Access: | http://hdl.handle.net/1721.1/102245 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
| _version_ | 1811094485830991872 |
|---|---|
| author | Indic, Premananda Bloch-Salisbury, Elisabeth Bednarek, Frank Brown, Emery N. Paydarfar, David Barbieri, Riccardo |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Indic, Premananda Bloch-Salisbury, Elisabeth Bednarek, Frank Brown, Emery N. Paydarfar, David Barbieri, Riccardo |
| author_sort | Indic, Premananda |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:00:45Z |
| format | Article |
| id | mit-1721.1/102245 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:00:45Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1022452022-09-29T17:41:59Z Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis Indic, Premananda Bloch-Salisbury, Elisabeth Bednarek, Frank Brown, Emery N. Paydarfar, David Barbieri, Riccardo Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Barbieri, Riccardo 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) 2016-04-15T19:25:14Z 2016-04-15T19:25:14Z 2011-07 2011-03 Article http://purl.org/eprint/type/JournalArticle 03783782 http://hdl.handle.net/1721.1/102245 Indic, Premananda, Elisabeth Bloch-Salisbury, Frank Bednarek, Emery N. Brown, David Paydarfar, and Riccardo Barbieri. “Assessment of Cardio-Respiratory Interactions in Preterm Infants by Bivariate Autoregressive Modeling and Surrogate Data Analysis.” Early Human Development 87, no. 7 (July 2011): 477–487. https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X en_US http://dx.doi.org/10.1016/j.earlhumdev.2011.04.001 Early Human Development Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Indic, Premananda Bloch-Salisbury, Elisabeth Bednarek, Frank Brown, Emery N. Paydarfar, David Barbieri, Riccardo Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title | Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title_full | Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title_fullStr | Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title_full_unstemmed | Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title_short | Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| title_sort | assessment of cardio respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis |
| url | http://hdl.handle.net/1721.1/102245 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
| work_keys_str_mv | AT indicpremananda assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis AT blochsalisburyelisabeth assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis AT bednarekfrank assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis AT brownemeryn assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis AT paydarfardavid assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis AT barbieririccardo assessmentofcardiorespiratoryinteractionsinpreterminfantsbybivariateautoregressivemodelingandsurrogatedataanalysis |