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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, Prof. Tirupati Goskula
Discrete wavelet transform, labor time detection, term and pre-term pregnancy, Support Vector Machines, EHG
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Published in : Volume 3 | Issue 2 | January-February 2017
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
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Uzma Fatima, Prof. Tirupati Goskula, "EHG Signal Classification for Term and Pre-Term Pregnancy Analysis", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.44-52, January-February-2017
URL : http://ijsrst.com/NCAEAS2312