Publication

Abstract : Preterm birth is regarded as one of the major causes of new-born illness and death. It not only creates a physical trauma but also a big mental trauma, especially to the new mother who has gone through a lot of changes throughout her body and mind. Although there are many diagnostic tools available for the early identification of preterm deliveries, many risks are associated with those and sometimes they fail to detect whether the delivery would happen as preterm or not. No chance can be taken by following invasive methods for introspection. After analysing different non-invasive methods, using Electro hysterogram, external recording of the uterine muscular activities is spotted as a great tool in detection of early birth. EHG measures the electrical activity of uterine muscle cells so is linked to the powerful contractions that occur during pregnancy. Combining this technique of acquiring EHG signals along with the Machine learning concepts makes the detection easier. Here, this research work presents a classification based on this approach using ten different classifiers and has arrived at a conclusion based on their performances.