Name
Title | ||
|---|---|---|
A review of significant researches on prediction of preterm birth using uterine electromyogram signal |
Abstract | ||
|---|---|---|
Early diagnose for the prevention of preterm birth is one of the important perinatal challenges. The neonatal care and early treatment for preterm babies are increasing the chance of survival, but anyways it affects the respiratory distress, immature brains, cerebral palsy, mental retardation, visual and hearing impairments, and poor health and growth. If preterm labor is diagnosed in the early period of gestation, then it is easy to give an appropriate treatment to the pregnant woman. The uterine electrical activity assessment is a suitable method for monitoring the labor process especially for the prediction of preterm labor. Electrohysterography is a non-invasive technique to monitor the contraction. The electrohysterogram (EHG) or uterine electromyogram (Uterine EMG) is considered as a biomarker for the prediction or preterm labor. A number of studies in this field by various researchers have been reviewed. On the basis of such reviews, this paper provides the different steps such as pre-processing , feature extraction, classifiers and feature subset selection methods for the detection and prediction of preterm birth. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2018.10.033 | Future Generation Computer Systems |
Keywords | Field | DocType |
Preterm,Electrohysterogram (EHG),Uterine electromyogram (UEMG),Pre-processing,Feature extraction,Feature subset selection,Classifier | Respiratory distress,Computer science,Cerebral palsy,Gestation,Real-time computing,Biomarker (medicine),Obstetrics | Journal |
Volume | ISSN | Citations |
98 | 0167-739X | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P Shaniba Asmi | 1 | 0 | 0.34 |
| Kamalraj Subramaniam | 2 | 4 | 5.15 |
| Nisheena V. Iqbal | 3 | 0 | 0.34 |