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dc.contributor.authorSammon, Michel
dc.contributor.authorCurley, Frederick J.
dc.date2022-08-11T08:10:03.000
dc.date.accessioned2022-08-23T16:53:30Z
dc.date.available2022-08-23T16:53:30Z
dc.date.issued1997-09-18
dc.date.submitted2008-08-04
dc.identifier.citation<p>J Appl Physiol. 1997 Sep;83(3):975-93.</p>
dc.identifier.issn8750-7587 (Print)
dc.identifier.doi10.1152/jappl.1997.83.3.975
dc.identifier.pmid9292487
dc.identifier.urihttp://hdl.handle.net/20.500.14038/42288
dc.description.abstractAutocorrelation function (C1) or autoregressive model parameters are often estimated for temporal analysis of physiological measurements. However, statistical approximations truncated at linear terms are unlikely to be of sufficient accuracy for patients whose homeostatic control systems cannot be presumed to be stable local to a single equilibrium. Thus a quadratic variant of C1 [autoskewness function (C2)] is introduced to detect nonlinearities in an output signal as a function of time delays. By use of simulations of nonlinear autoregressive models, C2 is shown to identify only those nonlinearities that "break" the symmetry of a system, altering the mean and skewness of its outputs. Case studies of patients with cardiopulmonary dysfunction demonstrate a range of ventilatory patterns seen in the clinical environment; whereas testing of C1 reveals their breath-by-breath minute ventilation to be significantly autocorrelated, the C2 test concludes that the correlation is nonlinear and asymmetrically distributed. Higher-order functionals [e.g., autokurtosis (C3)] are necessary for global analysis of metastable systems that continuously "switch" between multiple equilibrium states and unstable systems exhibiting nonequilibrium dynamics.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=9292487&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1152/jappl.1997.83.3.975
dc.subjectAlgorithms
dc.subjectHeart Failure
dc.subjectHeart Rate
dc.subjectHumans
dc.subjectModels, Biological
dc.subject*Nonlinear Dynamics
dc.subjectPlethysmography
dc.subjectRespiratory Mechanics
dc.subjectStochastic Processes
dc.subjectLife Sciences
dc.subjectMedicine and Health Sciences
dc.titleNonlinear systems identification: autocorrelation vs. autoskewness
dc.typeArticle
dc.source.journaltitleJournal of applied physiology (Bethesda, Md. : 1985)
dc.source.volume83
dc.source.issue3
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/651
dc.identifier.contextkey564459
html.description.abstract<p>Autocorrelation function (C1) or autoregressive model parameters are often estimated for temporal analysis of physiological measurements. However, statistical approximations truncated at linear terms are unlikely to be of sufficient accuracy for patients whose homeostatic control systems cannot be presumed to be stable local to a single equilibrium. Thus a quadratic variant of C1 [autoskewness function (C2)] is introduced to detect nonlinearities in an output signal as a function of time delays. By use of simulations of nonlinear autoregressive models, C2 is shown to identify only those nonlinearities that "break" the symmetry of a system, altering the mean and skewness of its outputs. Case studies of patients with cardiopulmonary dysfunction demonstrate a range of ventilatory patterns seen in the clinical environment; whereas testing of C1 reveals their breath-by-breath minute ventilation to be significantly autocorrelated, the C2 test concludes that the correlation is nonlinear and asymmetrically distributed. Higher-order functionals [e.g., autokurtosis (C3)] are necessary for global analysis of metastable systems that continuously "switch" between multiple equilibrium states and unstable systems exhibiting nonequilibrium dynamics.</p>
dc.identifier.submissionpathoapubs/651
dc.contributor.departmentDivision of Pulmonary and Critical Care Medicine
dc.source.pages975-93


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