DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
||||||||
Issue |
Volume-6, Issue-12 ( Dec, 2018 ) | |||||||||
Paper Title |
Estimation of APGAR Scoring by Artificial Neural Networks | |||||||||
Author Name |
Baris Doruk Gungor, Mehmet Recepbozkurt | |||||||||
Affilition |
Kocaeli University, Turkey Sakarya University, Turkey | |||||||||
Pages |
7-9 | |||||||||
Abstract |
To determine the effect of obstetric anesthesia on infants, It was developed in 1953 by Virginia Apgar. It is also an important method in terms of neurological development as far as the baby’s physical health is concerned. This method, which consists of 5 criteria, is used in the evaluation of the fetal condition, nst, cst, oct, amniotic fluid index, doppler, umbilical cord and cord blood gas analysis methods, as well as Apgar Score, are frequently used. In this study, maternal and fetal physiological data and attributes extracted from FHR (fetal heart rate) and UC (uterine contraction) signals were examined for a prenatal determination whether an intervention was needed for newborn baby and studies were made for Apgar Scoring. Keywords: Apgar Score, Cardiotocography, Neural Networks, SPSS Analysis, Mann-Whitney U Test. | |||||||||
| View Paper | ||||||||||
