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Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes.
MedLine Citation:
PMID:  21250912     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Objective. A model construction for classification of women with normal, hypertensive and preeclamptic pregnancy in different gestational ages using maternal heart rate variability (HRV) indexes. Method and patients. In the present work, we applied the artificial neural network for the classification problem, using the signal composed by the time intervals between consecutive RR peaks (RR) (n = 568) obtained from ECG records. Beside the HRV indexes, we also considered other factors like maternal history and blood pressure measurements. Results and conclusions. The obtained result reveals sensitivity for preeclampsia around 80% that increases for hypertensive and normal pregnancy groups. On the other hand, specificity is around 85-90%. These results indicate that the combination of HRV indexes with artificial neural networks (ANN) could be helpful for pregnancy study and characterization.
Authors:
Eduardo Tejera; Maria Jose Areias; Ana Rodrigues; Ana Ramõa; Jose Manuel Nieto-Villar; Irene Rebelo
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-1-21
Journal Detail:
Title:  The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians     Volume:  -     ISSN:  1476-4954     ISO Abbreviation:  -     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-1-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101136916     Medline TA:  J Matern Fetal Neonatal Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Biochemistry Department, Pharmacy Faculty Porto University, Portugal.
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