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.
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: M L Martinez-Fierro; G P Hernández-Delgadillo; V Flores-Morales; E Cardenas-Vargas; M Mercado-Reyes; I P Rodriguez-Sanchez; I Delgado-Enciso; C E Galván-Tejada; J I Galván-Tejada; J M Celaya-Padilla; I Garza-Veloz Journal: Exp Biol Med (Maywood) Date: 2018-02-07
Authors: Chayakrit Krittanawong; Andrew S Bomback; Usman Baber; Sripal Bangalore; Franz H Messerli; W H Wilson Tang Journal: Curr Hypertens Rep Date: 2018-07-06 Impact factor: 5.369
Authors: Tammy Y Euliano; Kostas Michalopoulos; Savyasachi Singh; Anthony R Gregg; Mariem Del Rio; Terrie Vasilopoulos; Amber M Johnson; Allison Onkala; Shalom Darmanjian; Neil R Euliano; Monique Ho Journal: Anesth Analg Date: 2018-03 Impact factor: 5.108
Authors: Rocco J LaFaro; Suryanarayana Pothula; Keshar Paul Kubal; Mario Emil Inchiosa; Venu M Pothula; Stanley C Yuan; David A Maerz; Lucresia Montes; Stephen M Oleszkiewicz; Albert Yusupov; Richard Perline; Mario Anthony Inchiosa Journal: PLoS One Date: 2015-12-28 Impact factor: 3.240
Authors: Ferdinand Dhombres; Jules Bonnard; Kévin Bailly; Paul Maurice; Aris T Papageorghiou; Jean-Marie Jouannic Journal: J Med Internet Res Date: 2022-04-20 Impact factor: 7.076
Authors: Manish Sharma; Jaypal Singh Rajput; Ru San Tan; U Rajendra Acharya Journal: Int J Environ Res Public Health Date: 2021-05-29 Impact factor: 3.390