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Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive

V Baier1, M Baumert, P Caminal

  • 1University of Applied Sciences Jena, Department of Medical Engineering, Germany.

IEEE Transactions on Bio-Medical Engineering
|January 13, 2006
PubMed

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.

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