| Title | Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. | ||
| Author | Tejera, Eduardo; Jose Areias, Maria; Rodrigues, Ana; Ramoa, Ana; Manuel Nieto-Villar, Jose; Rebelo, Irene | ||
| Journal | J Matern Fetal Neonatal Med | Publication Year/Month | 2011-Sep |
| PMID | 21250912 | PMCID | -N/A- |
| Affiliation | 1.Biochemistry Department, Pharmacy Faculty Porto University, Portugal. | ||
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.