Manuscript Number : NCAEAS2312
EHG Signal Classification for Term and Pre-Term Pregnancy Analysis
Authors(2) :-Uzma Fatima, Prof. Tirupati Goskula
Early prediction of premature pregnancy reduces neonatal death and helps in adoption of treatment well suited for the pre-term pregnancy state. There are scads of work done in the area of term and pre-term pregnancy analysis like artificial intelligence, regressive models, and higher order statistical models. This paper proposes a four-level decomposition of Electrohysterography (EHG) signals using Discrete Wavelet Transform (DWT) based on pyramid algorithm to obtain the final feature vector matrix. Classification is done using Support Vector Machines (SVM) by dividing the data into test and training sets. It is validated on a well-known benchmark database from Physionet Database. The proposed method can be used for real time implementation owing to low computational cost, high speed and its feasibility to be implemented on hardware. The encouraging experimental results show that the technique gives an accuracy of 97.8% and can be a promising tool for investigating the risk of preterm labor.
Uzma Fatima
Discrete wavelet transform, labor time detection, term and pre-term pregnancy, Support Vector Machines, EHG
Publication Details
Published in :
Volume 3 | Issue 2 | January-February 2017 Article Preview
Department of Electronics and Telecom, Anjuman College of Engg and Tech, Nagpur, Maharashtra, India
Prof. Tirupati Goskula
Department of Electronics and Telecom, Anjuman College of Engg and Tech, Nagpur, Maharashtra, India
WHO (2012) Born too soon: The Global Action Report on Preterm Birth.
Date of Publication :
2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
44-52
Manuscript Number :
NCAEAS2312
Publisher : Technoscience Academy
Journal URL :
http://ijsrst.com/NCAEAS2312