| Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. | |
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MedLine Citation:
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PMID: 21250912 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Eduardo Tejera; Maria Jose Areias; Ana Rodrigues; Ana Ramõa; Jose Manuel Nieto-Villar; Irene Rebelo |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-1-21 |
Journal Detail:
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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:
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Created Date: 2011-1-21 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101136916 Medline TA: J Matern Fetal Neonatal Med Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Biochemistry Department, Pharmacy Faculty Porto University, Portugal. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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