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Author Baier, V. ♦ Baumert, M. ♦ Caminal, P. ♦ Vallverdu, M. ♦ Faber, R. ♦ Voss, A.
Sponsorship IEEE Engineering in Medicine and Biology Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1964
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health ♦ Engineering & allied operations
Subject Keyword Hidden Markov models ♦ Cardiology ♦ Hypertension ♦ Pregnancy ♦ Pressure control ♦ Heart rate variability ♦ Blood pressure ♦ Cardiovascular system ♦ Biomedical engineering ♦ Blood pressure variability ♦ pregnancy induced hypertension ♦ Blood pressure variability ♦ cardiovascular control ♦ heart rate variability ♦ hidden Markov model ♦ preeclampsia
Abstract Discrete 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.
Description Author affiliation :: Dept. of Med. Eng., Univ. of Appl. Sci. Jena, Germany
ISSN 00189294
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-01-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 53
Issue Number 1
Size (in Bytes) 106.28 kB
Page Count 4
Starting Page 140
Ending Page 143


Source: IEEE Xplore Digital Library