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

被引：8

作者：

Baier, V

Baumert, M

Caminal, P

Vallverdú, M

Faber, R

Voss, A

机构：

[1] Univ Appl Sci Jena, Dept Med Engn, D-07745 Jena, Germany

[2] Tech Univ Catalonia, Dept ESAII, Biomed Engn Res Ctr, Barcelona, Spain

[3] Univ Leipzig, Dept Obstet & Gynecol, D-04109 Leipzig, Germany

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2006年 / 53卷 / 01期

关键词：

blood pressure variability; cardiovascular control; heart rate variability; hidden Markov model; preeclampsia; pregnancy induced hypertension;

D O I：

10.1109/TBME.2005.859812

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：140 / 143

页数：4

共 32 条

[1]

[Anonymous], HIDDEN MARKOV MODEL

[2] STATISTICAL INFERENCE FOR PROBABILISTIC FUNCTIONS OF FINITE STATE MARKOV CHAINS [J].