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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/36605
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dc.contributor.authorBaier, V.-
dc.contributor.authorBaumert, M.-
dc.contributor.authorCaminal, P.-
dc.contributor.authorVallverdu, M.-
dc.contributor.authorFaber, R.-
dc.contributor.authorVoss, A.-
dc.date.issued2006-
dc.identifier.citationIEEE Transactions on Biomedical Engineering, 2006; 53(1):140-143-
dc.identifier.issn0018-9294-
dc.identifier.issn1558-2531-
dc.identifier.urihttp://hdl.handle.net/2440/36605-
dc.descriptionCopyright © 2006 IEEE-
dc.description.abstractDiscrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.-
dc.description.statementofresponsibilityV. Baier, M. Baumert, P. Caminal, M. Vallverdú, R. Faber, and A. Voss-
dc.language.isoen-
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc-
dc.source.urihttp://dx.doi.org/10.1109/tbme.2005.859812-
dc.subjectHumans-
dc.subjectHypertension, Pregnancy-Induced-
dc.subjectDiagnosis, Computer-Assisted-
dc.subjectBlood Pressure Determination-
dc.subjectElectrocardiography-
dc.subjectModels, Statistical-
dc.subjectMarkov Chains-
dc.subjectPregnancy-
dc.subjectBlood Pressure-
dc.subjectHeart Rate-
dc.subjectAlgorithms-
dc.subjectModels, Cardiovascular-
dc.subjectFeedback-
dc.subjectComputer Simulation-
dc.subjectPattern Recognition, Automated-
dc.subjectFemale-
dc.subjectStatistics as Topic-
dc.titleHidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders-
dc.typeJournal article-
dc.identifier.doi10.1109/TBME.2005.859812-
pubs.publication-statusPublished-
dc.identifier.orcidBaumert, M. [0000-0003-2984-2167]-
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Electrical and Electronic Engineering publications

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