A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis
Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pre...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2012
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| Online Access: | http://hdl.handle.net/1721.1/70072 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
| _version_ | 1811084917525708800 |
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| author | Chen, Zhe Purdon, Patrick Lee Brown, Emery N. Barbieri, Riccardo |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Chen, Zhe Purdon, Patrick Lee Brown, Emery N. Barbieri, Riccardo |
| author_sort | Chen, Zhe |
| collection | MIT |
| description | Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions. |
| first_indexed | 2024-09-23T12:59:40Z |
| format | Article |
| id | mit-1721.1/70072 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:59:40Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/700722022-10-01T12:23:01Z A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis Chen, Zhe Purdon, Patrick Lee Brown, Emery N. Barbieri, Riccardo Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Brown, Emery N. Chen, Zhe Purdon, Patrick Lee Barbieri, Riccardo Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions. National Institutes of Health (U.S.) (Grant R01-HL084502) National Institutes of Health (U.S.) (Grant K25-NS05758) National Institutes of Health (U.S.) (Grant DP2-OD006454) National Institutes of Health (U.S.) (Grant DP1-OD003646) National Institutes of Health (U.S.) (Grant CRC UL1 RR025758) 2012-04-20T15:06:56Z 2012-04-20T15:06:56Z 2010-11 2010-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-4123-5 1557-170X INSPEC Accession Number: 11659977 http://hdl.handle.net/1721.1/70072 Zhe Chen et al. “A Differential Autoregressive Modeling Approach Within a Point Process Framework for Non-stationary Heartbeat Intervals Analysis.” IEEE, 2010. 3567–3570. Web. ©2010 IEEE. 21096829 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X en_US http://dx.doi.org/10.1109/IEMBS.2010.5627462 Proceedings of the 32rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2010 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Chen, Zhe Purdon, Patrick Lee Brown, Emery N. Barbieri, Riccardo A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title | A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title_full | A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title_fullStr | A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title_full_unstemmed | A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title_short | A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis |
| title_sort | differential autoregressive modeling approach within a point process framework for non stationary heartbeat intervals analysis |
| url | http://hdl.handle.net/1721.1/70072 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
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