A Review on Prediction of Preterm Deliveries from Uterine Electrohysterography (EHG) Signals Using Support Vector Machine (SVM)
Khwaja Ramizuddin, Akhilesh Upadhyay
Birth, Electrohysterography, Deliveries, Prediction, Preterm.
The aim of this research work is to develop a system, which will classify between Prediction of Preterm and term Deliveries from Uterine Electrohysterography(EHG) signals using Support Vector Machine(SVM). For the said purpose statistical and non-linear features can be extracted and can be applied for classification with SVM based algorithm. This system will automate the diagnosis process with adequate accuracy and improvement in the treatment of preterm infants which will help to increase their chances of survival. Currently, majority of the methods to predict preterm birth are nonobjective. Further, an effective and powerful proof advocates the analysis of uterine electrical signals (Electrohysterography) that could come up with a feasible way of diagnosing true labor and predict preterm deliveries. In this work focus will be on to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper investigates above stated proposition forward and facilitates a method that includes machine learning method and clearly separates records of term and preterm using an open source dataset.
Article Details
Unique Paper ID: 149326

Publication Volume & Issue: Volume 6, Issue 11

Page(s): 679 - 681
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Last Date 25 February 2020

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