title: Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders.
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.
authors.affiliation: University of Applied Sciences Jena, Department of Medical Engineering, Germany.
authors.forename: V
authors.initials: V
authors.lastname: Baier
authors.forename: M
authors.initials: M
authors.lastname: Baumert
authors.forename: P
authors.initials: P
authors.lastname: Caminal
authors.forename: M
authors.initials: M
authors.lastname: Vallverdú
authors.forename: R
authors.initials: R
authors.lastname: Faber
authors.forename: A
authors.initials: A
authors.lastname: Voss
mesh_terms: D000465:Algorithms; D001794:Blood Pressure; D001795:Blood Pressure Determination / Q000379:methods; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000379:methods; D005246:Feedback; D005260:Female; D006339:Heart Rate; D006801:Humans; D046110:Hypertension, Pregnancy-Induced / Q000175:diagnosis* / Q000503:physiopathology*; D008390:Markov Chains; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011247:Pregnancy; D013223:Statistics as Topic