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 · 2011
Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes.
Оценочное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Tejera Eduardo, Jose Areias Maria, Rodrigues Ana, Ramõa Ana, Manuel Nieto-Villar Jose, Rebelo Irene
Аннотация
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.
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.