Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework
Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/67466 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
| _version_ | 1826217800169947136 |
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| author | Chen, Zhe 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 Brown, Emery N. Barbieri, Riccardo |
| author_sort | Chen, Zhe |
| collection | MIT |
| description | Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings. |
| first_indexed | 2024-09-23T17:09:21Z |
| format | Article |
| id | mit-1721.1/67466 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:09:21Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/674662022-09-30T00:01:58Z Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework Chen, Zhe 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 Barbieri, Riccardo Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings. 2011-12-06T20:46:54Z 2011-12-06T20:46:54Z 2010-06 Article http://purl.org/eprint/type/JournalArticle 0018-9294 INSPEC Accession Number: 11340778 http://hdl.handle.net/1721.1/67466 Zhe Chen, E.N. Brown, and R. Barbieri. “Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework.” Biomedical Engineering, IEEE Transactions on 57.6 (2010): 1335-1347. © 2011 IEEE. PubMed ID: 20172783 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X en_US http://dx.doi.org/10.1109/tbme.2010.2041002 IEEE Transactions on Biomedical Engineering 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 Brown, Emery N. Barbieri, Riccardo Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title | Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title_full | Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title_fullStr | Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title_full_unstemmed | Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title_short | Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework |
| title_sort | characterizing nonlinear heartbeat dynamics within a point process framework |
| url | http://hdl.handle.net/1721.1/67466 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0002-6166-448X |
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