Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/36605
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Type: Journal article
Title: Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders
Author: Baier, V.
Baumert, M.
Caminal, P.
Vallverdu, M.
Faber, R.
Voss, A.
Citation: IEEE Transactions on Biomedical Engineering, 2006; 53(1):140-143
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2006
ISSN: 0018-9294
1558-2531
Statement of
Responsibility: 
V. Baier, M. Baumert, P. Caminal, M. Vallverdú, R. Faber, and A. Voss
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.
Keywords: Humans
Hypertension, Pregnancy-Induced
Diagnosis, Computer-Assisted
Blood Pressure Determination
Electrocardiography
Models, Statistical
Markov Chains
Pregnancy
Blood Pressure
Heart Rate
Algorithms
Models, Cardiovascular
Feedback
Computer Simulation
Pattern Recognition, Automated
Female
Statistics as Topic
Description: Copyright © 2006 IEEE
DOI: 10.1109/TBME.2005.859812
Appears in Collections:Aurora harvest 6
Electrical and Electronic Engineering publications

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